title
Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4

description
Welcome to part 4 of the Reinforcement Learning series as well our our Q-learning part of it. In this part, we're going to wrap up this basic Q-Learning by making our own environment to learn in. I hadn't initially intended to do this as a tutorial, it was just something I personally wanted to do, but, after many requests, it only makes sense to do it as a tutorial! Text-based tutorial and sample code: https://pythonprogramming.net/own-environment-q-learning-reinforcement-learning-python-tutorial/ Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex #reinforcementlearning #machinelearning #python

detail
{'title': 'Creating A Reinforcement Learning (RL) Environment - Reinforcement Learning p.4', 'heatmap': [{'end': 862.916, 'start': 829.734, 'weight': 0.864}, {'end': 2790.334, 'start': 2751.807, 'weight': 1}], 'summary': 'Tutorial series covers building a q-learning environment with player, food, and enemy blobs, setting up opencv and creating a 10x10 grid-based environment, implementing q-learning algorithm for environment navigation, and analyzing qtable performance in different scenarios.', 'chapters': [{'end': 122.795, 'segs': [{'end': 44.634, 'src': 'embed', 'start': 18.482, 'weight': 1, 'content': [{'end': 24.163, 'text': "And I didn't really intend to make a tutorial out of it, but people were asking and then it's kind of like well, obviously that's what I wanted to do,", 'start': 18.482, 'duration': 5.681}, {'end': 25.964, 'text': "so of course that's what other people probably want to do too.", 'start': 24.163, 'duration': 1.801}, {'end': 28.105, 'text': 'So anyway, here we are.', 'start': 26.864, 'duration': 1.241}, {'end': 29.365, 'text': "Let's make our own environment.", 'start': 28.285, 'duration': 1.08}, {'end': 34.768, 'text': "So I'm actually just going to kind of run through the environment that I made and explain it as I go.", 'start': 29.466, 'duration': 5.302}, {'end': 37.89, 'text': "So we're just going to do the program linearly.", 'start': 34.928, 'duration': 2.962}, {'end': 44.634, 'text': 'But if you have questions, whatever, as always, comment below, join the discord.gg slash syntax.', 'start': 38.791, 'duration': 5.843}], 'summary': 'Creating a tutorial for making own environment with linear program.', 'duration': 26.152, 'max_score': 18.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU18482.jpg'}, {'end': 90.335, 'src': 'embed', 'start': 59.409, 'weight': 0, 'content': [{'end': 63.053, 'text': 'every I use blobs for like examples of everything.', 'start': 59.409, 'duration': 3.644}, {'end': 68.659, 'text': "so if you haven't noticed by now, I'm a blob of file and Anyway.", 'start': 63.053, 'duration': 5.606}, {'end': 75.944, 'text': "so I always want to make blob things, and so the idea was to make blobs, and in this case You've got a player blob, a food blob.", 'start': 68.659, 'duration': 7.285}, {'end': 76.765, 'text': "That's the objective.", 'start': 75.984, 'duration': 0.781}, {'end': 84.05, 'text': "We're trying to achieve and then, just for you know Complexity, I added an enemy blob as well.", 'start': 76.805, 'duration': 7.245}, {'end': 90.335, 'text': "so the idea is that you have this player blob and and we'll start by having the enemy and the food not move.", 'start': 84.05, 'duration': 6.285}], 'summary': 'Creating blobs for player, food, and enemy with no movement.', 'duration': 30.926, 'max_score': 59.409, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU59409.jpg'}], 'start': 1.437, 'title': 'Building q-learning environment', 'summary': 'Introduces the process of creating a custom q-learning environment with player, food, and enemy blobs, focusing on simplicity and self-explanatory design.', 'chapters': [{'end': 122.795, 'start': 1.437, 'title': 'Building q-learning environment', 'summary': 'Introduces building a custom q-learning environment with player, food, and enemy blobs, emphasizing the importance of creating a self-explanatory and not overly complex environment.', 'duration': 121.358, 'highlights': ['The chapter introduces building a custom Q-Learning environment with player, food, and enemy blobs It describes the process of creating a custom environment for Q-Learning, involving player, food, and enemy blobs, laying the foundation for further development.', 'Emphasizes the importance of creating a self-explanatory and not overly complex environment The author stresses the need for the environment to be self-explanatory and not overly complex, making it easier for others to understand and utilize.', 'Discusses the relevance of player, food, and enemy blobs in the environment The chapter explains the significance of using player, food, and enemy blobs within the environment, providing a clear objective and adding complexity for learning purposes.']}], 'duration': 121.358, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1437.jpg', 'highlights': ['The chapter introduces building a custom Q-Learning environment with player, food, and enemy blobs.', 'Emphasizes the importance of creating a self-explanatory and not overly complex environment.', 'Discusses the relevance of player, food, and enemy blobs in the environment.']}, {'end': 610.376, 'segs': [{'end': 186.261, 'src': 'embed', 'start': 154.025, 'weight': 0, 'content': [{'end': 161.768, 'text': 'OpenCV-python Okay, I feel like I always get that wrong.', 'start': 154.025, 'duration': 7.743}, {'end': 165.89, 'text': 'Anyway, okay, pip install opencv-python.', 'start': 162.568, 'duration': 3.322}, {'end': 172.694, 'text': 'If for whatever reason, python-opencv did install for you, you probably just installed something really bad.', 'start': 166.371, 'duration': 6.323}, {'end': 174.715, 'text': "So don't use it, get rid of it.", 'start': 172.894, 'duration': 1.821}, {'end': 176.857, 'text': 'Moving along.', 'start': 176.416, 'duration': 0.441}, {'end': 179.538, 'text': 'So, all right.', 'start': 178.097, 'duration': 1.441}, {'end': 182.579, 'text': 'So we are also going to use NumPy, but you should already have that.', 'start': 180.198, 'duration': 2.381}, {'end': 186.261, 'text': "And then we're going to use the Python imaging library.", 'start': 182.779, 'duration': 3.482}], 'summary': "Install opencv-python using 'pip install opencv-python', avoid python-opencv, and also use numpy and python imaging library.", 'duration': 32.236, 'max_score': 154.025, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU154025.jpg'}, {'end': 274.5, 'src': 'embed', 'start': 247.492, 'weight': 1, 'content': [{'end': 258.927, 'text': "We're gonna use time purely to set dynamic cue table file names And so it has like some order What's your deal? Probably too undefined.", 'start': 247.492, 'duration': 11.435}, {'end': 260.408, 'text': 'Oh, typo style.', 'start': 259.187, 'duration': 1.221}, {'end': 262.089, 'text': 'Okay, great.', 'start': 261.629, 'duration': 0.46}, {'end': 265.253, 'text': "So the first thing I'm going to do is just try to run this real quick.", 'start': 262.67, 'duration': 2.583}, {'end': 267.014, 'text': 'Make sure it actually runs.', 'start': 265.593, 'duration': 1.421}, {'end': 267.715, 'text': 'It does.', 'start': 267.234, 'duration': 0.481}, {'end': 270.537, 'text': 'Okay, so all our imports worked.', 'start': 268.496, 'duration': 2.041}, {'end': 271.678, 'text': "And we're good to go.", 'start': 271.178, 'duration': 0.5}, {'end': 274.5, 'text': "So now we're going to have some.", 'start': 272.359, 'duration': 2.141}], 'summary': 'Testing successful, all imports worked, ready to proceed.', 'duration': 27.008, 'max_score': 247.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU247492.jpg'}, {'end': 325.5, 'src': 'embed', 'start': 296.493, 'weight': 2, 'content': [{'end': 298.294, 'text': "So in this case, it's going to be a 10 by 10 grid.", 'start': 296.493, 'duration': 1.801}, {'end': 307.346, 'text': 'So the player, the food, and the enemy will be initialized at a random location on a 10x10, but we can change this as time goes on.', 'start': 299.935, 'duration': 7.411}, {'end': 312.974, 'text': "I'll talk about how some of those changes will impact things, but obviously, as you increase this size,", 'start': 307.386, 'duration': 5.588}, {'end': 315.418, 'text': 'especially depending on the size of your action space as well,', 'start': 312.974, 'duration': 2.444}, {'end': 323.66, 'text': 'that is going to just exponentially explode the number of possible combinations in your cue table.', 'start': 316.018, 'duration': 7.642}, {'end': 325.5, 'text': "So anyway, we'll start with a 10 by 10.", 'start': 323.76, 'duration': 1.74}], 'summary': 'Discussion on a 10x10 grid for game initialization and the impact of size on cue table combinations.', 'duration': 29.007, 'max_score': 296.493, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU296493.jpg'}, {'end': 387.586, 'src': 'embed', 'start': 343.785, 'weight': 3, 'content': [{'end': 348.926, 'text': "So this is if we hit the enemy we're gonna say is a 300, so we'll subtract that penalty.", 'start': 343.785, 'duration': 5.141}, {'end': 351.407, 'text': "basically, we're going to say a food reward.", 'start': 348.926, 'duration': 2.481}, {'end': 353.867, 'text': 'uh, 25 this is.', 'start': 351.407, 'duration': 2.46}, {'end': 356.108, 'text': "i haven't really decided where i want this.", 'start': 353.867, 'duration': 2.241}, {'end': 360.849, 'text': "i don't know if i really want it to be one, a zero, like we had with the mountain car.", 'start': 356.108, 'duration': 4.741}, {'end': 362.75, 'text': "25, i haven't really decided.", 'start': 360.849, 'duration': 1.901}, {'end': 365.431, 'text': "i don't really know what's the best way to what's the best one to use.", 'start': 362.75, 'duration': 2.681}, {'end': 366.671, 'text': "so i'm just gonna throw in 25.", 'start': 365.431, 'duration': 1.24}, {'end': 370.232, 'text': "um, i haven't noticed anything.", 'start': 366.671, 'duration': 3.561}, {'end': 372.933, 'text': 'you know huge to tell me one way or the other.', 'start': 370.232, 'duration': 2.701}, {'end': 378.698, 'text': "Then we're going to have epsilon lowercase because it's going to change over time.", 'start': 373.973, 'duration': 4.725}, {'end': 381.84, 'text': "We'll start at 0.9, another thing that we could change a ton.", 'start': 379.058, 'duration': 2.782}, {'end': 386.264, 'text': "Eps, decay, so it's epsilon, decay, 0.", 'start': 382.481, 'duration': 3.783}, {'end': 387.586, 'text': 'I went with 9998.', 'start': 386.264, 'duration': 1.322}], 'summary': 'Using a 300 penalty, food reward of 25, epsilon starts at 0.9, and epsilon decay at 0.9998.', 'duration': 43.801, 'max_score': 343.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU343785.jpg'}, {'end': 580.931, 'src': 'embed', 'start': 553.086, 'weight': 5, 'content': [{'end': 557.291, 'text': "Now I'm actually defining these colors in BGR format.", 'start': 553.086, 'duration': 4.205}, {'end': 560.995, 'text': 'Even though I was really trying to use RGB.', 'start': 558.492, 'duration': 2.503}, {'end': 566.501, 'text': 'So side project is if anybody can tell me why is it BGR.', 'start': 561.055, 'duration': 5.446}, {'end': 568.583, 'text': "Because I don't think it's like the.", 'start': 567.102, 'duration': 1.481}, {'end': 569.684, 'text': "I don't know.", 'start': 569.384, 'duration': 0.3}, {'end': 573.526, 'text': "Maybe I'll figure it out as we go, but that was a problem I haven't solved yet.", 'start': 569.824, 'duration': 3.702}, {'end': 576.027, 'text': "Anyway, it's 255, 0.", 'start': 573.727, 'duration': 2.3}, {'end': 580.931, 'text': "So the player, B, is mostly blue, some green, so it's kind of like a lightish blue.", 'start': 576.028, 'duration': 4.903}], 'summary': 'Defining colors in bgr format, 255,0. need to understand why bgr instead of rgb.', 'duration': 27.845, 'max_score': 553.086, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU553086.jpg'}], 'start': 123.175, 'title': 'Setting up opencv and creating a grid-based environment for reinforcement learning', 'summary': 'Covers the setup of opencv, numpy, and python imaging library, ensuring successful installation and import of required libraries. it also discusses parameters and settings for creating a 10x10 grid-based environment for reinforcement learning, including 25,000 episodes, penalties, rewards, initial epsilon value, decay rate, and color definitions for player, food, and enemy.', 'chapters': [{'end': 274.5, 'start': 123.175, 'title': 'Setting up opencv and required libraries', 'summary': 'Covers the setup of opencv, numpy, and python imaging library through pip installation and importing necessary modules, ensuring a successful installation and import of the required libraries for the upcoming demonstration.', 'duration': 151.325, 'highlights': ['Importing necessary modules and setting up the environment for OpenCV, NumPy, and Python Imaging Library. The speaker emphasizes the installation of OpenCV using pip, ensures the correct installation of opencv-python, and also mentions the usage of NumPy and Python Imaging Library for which he clarifies the pip installation of pillow.', 'Running a quick check to ensure successful import and installation of all required libraries. The speaker verifies the successful execution of the imports and concludes that all the required libraries have been imported and the setup is ready for further usage.']}, {'end': 610.376, 'start': 274.5, 'title': 'Creating a grid-based environment for reinforcement learning', 'summary': 'Discusses the parameters and settings for creating a grid-based environment for reinforcement learning, including a 10x10 grid size, 25,000 episodes, penalties for hitting the enemy, and rewards for finding food, as well as the initial epsilon value, decay rate, and color definitions for player, food, and enemy.', 'duration': 335.876, 'highlights': ['Setting the grid size to 10x10 and initializing the player, food, and enemy at random locations on the grid. Defining a 10x10 grid size for the environment, impacting the number of possible combinations in the cue table.', 'Specifying 25,000 episodes for the learning process and assigning penalties for hitting the enemy and rewards for finding food. Determining the number of episodes for training and establishing penalties and rewards, impacting the learning process.', 'Setting the initial epsilon value to 0.9 and the epsilon decay rate to 0.9998. Defining the initial exploration-exploitation trade-off and its decay rate, crucial for balancing exploration and exploitation during learning.', 'Defining the colors in BGR format for the player, food, and enemy in the grid-based environment. Assigning color representations for player, food, and enemy, impacting the visual representation of the environment.']}], 'duration': 487.201, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU123175.jpg', 'highlights': ['Importing necessary modules and setting up the environment for OpenCV, NumPy, and Python Imaging Library.', 'Running a quick check to ensure successful import and installation of all required libraries.', 'Defining a 10x10 grid size for the environment, impacting the number of possible combinations in the cue table.', 'Specifying 25,000 episodes for the learning process and assigning penalties for hitting the enemy and rewards for finding food.', 'Setting the initial epsilon value to 0.9 and the epsilon decay rate to 0.9998.', 'Defining the colors in BGR format for the player, food, and enemy in the grid-based environment.']}, {'end': 974.139, 'segs': [{'end': 663.576, 'src': 'embed', 'start': 611.956, 'weight': 0, 'content': [{'end': 620.522, 'text': 'Okay, so now what we need is we need a blob class because really all of these blobs are going to have a lot of the same attributes at least.', 'start': 611.956, 'duration': 8.566}, {'end': 622.323, 'text': "They're going to need to be able to move.", 'start': 620.882, 'duration': 1.441}, {'end': 623.724, 'text': "They're going to need a starting location.", 'start': 622.343, 'duration': 1.381}, {'end': 625.425, 'text': 'Like they need to be initialized randomly.', 'start': 623.744, 'duration': 1.681}, {'end': 627.687, 'text': "They're going to need to be able to be moved.", 'start': 625.905, 'duration': 1.782}, {'end': 634.174, 'text': "And then later, basically our observation, I didn't really want to use.", 'start': 628.108, 'duration': 6.066}, {'end': 640.201, 'text': 'Because you would have a huge observation space if you needed to pass what is the location of.', 'start': 634.174, 'duration': 6.027}, {'end': 650.508, 'text': 'I felt like the problem would be much more complex, I guess, if you passed the physical location of everything.', 'start': 642.403, 'duration': 8.105}, {'end': 652.549, 'text': 'So my plan is instead,', 'start': 650.588, 'duration': 1.961}, {'end': 663.576, 'text': 'the observation space is actually going to be the relative position of the food and then the relative position of the enemy to the player.', 'start': 652.549, 'duration': 11.027}], 'summary': 'Developing a blob class with specific attributes such as movement, starting location, and relative positions for observation space.', 'duration': 51.62, 'max_score': 611.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU611956.jpg'}, {'end': 738.501, 'src': 'embed', 'start': 684.042, 'weight': 2, 'content': [{'end': 687.743, 'text': 'And in this case, it seems to me that making a blob class is actually useful.', 'start': 684.042, 'duration': 3.701}, {'end': 690.344, 'text': 'So anyway, class blob.', 'start': 688.343, 'duration': 2.001}, {'end': 702.629, 'text': "So define our init method and if you don't know, object-oriented programming in Python, I would go to pythonprogramming.net,", 'start': 691.304, 'duration': 11.325}, {'end': 710.412, 'text': 'go to Python Fundamentals, Intermediate Python and then, in this series, we get into object-oriented programming here.', 'start': 702.629, 'duration': 7.783}, {'end': 715.024, 'text': "And I would strongly recommend you go through this if it's confusing to you.", 'start': 711.021, 'duration': 4.003}, {'end': 720.468, 'text': 'And then we also are going to do some operator overriding and stuff like that.', 'start': 715.925, 'duration': 4.543}, {'end': 723.65, 'text': "So you might want to check that out if that's confusing to you.", 'start': 720.548, 'duration': 3.102}, {'end': 727.333, 'text': 'So our init method, this is just going to run immediately.', 'start': 725.111, 'duration': 2.222}, {'end': 738.501, 'text': "So we're going to say self.x is going to be equal to numpy.random.randint between 0 and the size, all caps.", 'start': 727.413, 'duration': 11.088}], 'summary': 'Creating a blob class for object-oriented programming in python, with operator overriding and numpy.random.randint.', 'duration': 54.459, 'max_score': 684.042, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU684042.jpg'}, {'end': 862.916, 'src': 'heatmap', 'start': 815.49, 'weight': 4, 'content': [{'end': 817.151, 'text': 'Anyway, someone will correct me.', 'start': 815.49, 'duration': 1.661}, {'end': 818.171, 'text': 'Actually, it is.', 'start': 817.471, 'duration': 0.7}, {'end': 821.272, 'text': 'Anyway, return.', 'start': 819.771, 'duration': 1.501}, {'end': 824.913, 'text': 'So this just allows us to subtract a blob from another blob.', 'start': 821.312, 'duration': 3.601}, {'end': 827.693, 'text': 'So in fact, we need another blob.', 'start': 825.573, 'duration': 2.12}, {'end': 828.994, 'text': "So I'm going to say other blob.", 'start': 827.714, 'duration': 1.28}, {'end': 836.415, 'text': 'So when we subtract, when you do a minus and then a thing, that thing will get passed here.', 'start': 829.734, 'duration': 6.681}, {'end': 841.056, 'text': "And then in here, we're going to say return.", 'start': 836.995, 'duration': 4.061}, {'end': 843.837, 'text': "And in this case, what we're going to say is return.", 'start': 841.136, 'duration': 2.701}, {'end': 854.299, 'text': "And then in parentheses, we're going to say self.x minus other.x comma self.y minus other.y.", 'start': 844.037, 'duration': 10.262}, {'end': 856.939, 'text': 'Cool Cool.', 'start': 854.339, 'duration': 2.6}, {'end': 862.916, 'text': "Looks like Pep8 doesn't care either way if you want a space here or not.", 'start': 859.795, 'duration': 3.121}], 'summary': 'Code discussion involving subtraction of blobs in python, with reference to pep8 guidelines.', 'duration': 28.347, 'max_score': 815.49, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU815490.jpg'}, {'end': 904.033, 'src': 'embed', 'start': 877.763, 'weight': 5, 'content': [{'end': 881.805, 'text': 'The player is gonna take a discrete action, right?', 'start': 877.763, 'duration': 4.042}, {'end': 886.947, 'text': "so we're gonna have an action method, but really the players, the only one that's going to use it.", 'start': 882.225, 'duration': 4.722}, {'end': 890.948, 'text': 'but there might be a time where you want to have many players.', 'start': 886.947, 'duration': 4.001}, {'end': 893.709, 'text': "um so I'm just gonna.", 'start': 890.948, 'duration': 2.761}, {'end': 898.791, 'text': "I'm gonna go ahead and do this like in this, like Q learning, we're not going to have many players.", 'start': 893.709, 'duration': 5.082}, {'end': 904.033, 'text': "I don't think I can't really see a good reason why I would do that, but later we might want to do that and then,", 'start': 898.791, 'duration': 5.242}], 'summary': 'Discussion on implementing a discrete action method for a single player in q learning, with potential for multiple players in the future.', 'duration': 26.27, 'max_score': 877.763, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU877763.jpg'}], 'start': 611.956, 'title': 'Creating blob class', 'summary': 'Discusses the need for a blob class in game simulation and python programming, outlining attributes, functionalities, and methods for handling object-oriented programming.', 'chapters': [{'end': 663.576, 'start': 611.956, 'title': 'Creating blob class for game simulation', 'summary': 'Discusses the need for a blob class in a game simulation, outlining the attributes and functionalities it should have, and the rationale behind using relative positions for the observation space.', 'duration': 51.62, 'highlights': ['The blobs in the game simulation require a blob class with shared attributes such as the ability to move and a starting location, aiming for efficient implementation (e.g., minimizing observation space complexity).', 'Using relative positions of the food and enemy to the player as the observation space reduces the complexity of the problem, providing a more manageable approach for the game simulation.']}, {'end': 974.139, 'start': 664.116, 'title': 'Creating a blob class for python programming', 'summary': 'Discusses the creation of a blob class in python for handling object-oriented programming, including method initialization and operator overloading, with a focus on action and movement methods.', 'duration': 310.023, 'highlights': ['The chapter introduces the creation of a blob class in Python for handling object-oriented programming, with a focus on method initialization and operator overloading.', 'The method initialization involves defining the init method for initializing the x and y coordinates randomly using numpy.random.randint.', 'The chapter covers the implementation of operator overloading, including the subtraction operator, allowing the subtraction of one blob from another.', 'The discussion includes the implementation of action and movement methods for the player, allowing discrete action choices for movement.']}], 'duration': 362.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU611956.jpg', 'highlights': ['The blobs in the game simulation require a blob class with shared attributes such as the ability to move and a starting location, aiming for efficient implementation (e.g., minimizing observation space complexity).', 'Using relative positions of the food and enemy to the player as the observation space reduces the complexity of the problem, providing a more manageable approach for the game simulation.', 'The chapter introduces the creation of a blob class in Python for handling object-oriented programming, with a focus on method initialization and operator overloading.', 'The method initialization involves defining the init method for initializing the x and y coordinates randomly using numpy.random.randint.', 'The chapter covers the implementation of operator overloading, including the subtraction operator, allowing the subtraction of one blob from another.', 'The discussion includes the implementation of action and movement methods for the player, allowing discrete action choices for movement.']}, {'end': 1313.315, 'segs': [{'end': 1043.141, 'src': 'embed', 'start': 1013.844, 'weight': 1, 'content': [{'end': 1020.068, 'text': "So if it gets to the wall and then tries to move diagonally, it moves up or down, right? Or side to side, depending on which wall it's at.", 'start': 1013.844, 'duration': 6.224}, {'end': 1032.038, 'text': "Uh, so, so it still actually works and I'm still amazed that it learns to use the wall because as you'll see, it has no idea about the wall.", 'start': 1020.968, 'duration': 11.07}, {'end': 1033.64, 'text': "It doesn't know there's any boundary.", 'start': 1032.058, 'duration': 1.582}, {'end': 1035.957, 'text': 'to its environment.', 'start': 1034.076, 'duration': 1.881}, {'end': 1043.141, 'text': "so the fact that it learns to use the wall is fascinating, and i don't know if it's because because it's not smart enough.", 'start': 1035.957, 'duration': 7.184}], 'summary': "The ai learns to use walls, despite not knowing about boundaries. it's fascinating.", 'duration': 29.297, 'max_score': 1013.844, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1013844.jpg'}, {'end': 1092.77, 'src': 'embed', 'start': 1068.573, 'weight': 0, 'content': [{'end': 1076.177, 'text': 'And this model can be trained for like almost 100% because, like in theory, half of the time when it gets initialized randomly,', 'start': 1068.573, 'duration': 7.604}, {'end': 1079.898, 'text': 'the food should be in a location inaccessible without using the wall.', 'start': 1076.177, 'duration': 3.721}, {'end': 1085.061, 'text': 'But this model actually learns to succeed almost 100% of the time.', 'start': 1080.879, 'duration': 4.182}, {'end': 1089.185, 'text': "So that's mind boggling.", 'start': 1086.321, 'duration': 2.864}, {'end': 1092.77, 'text': 'But anyway, if you want to add more choices, go for it.', 'start': 1089.406, 'duration': 3.364}], 'summary': 'Model achieves nearly 100% success in learning to access inaccessible food location, a mind-boggling feat.', 'duration': 24.197, 'max_score': 1068.573, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1068573.jpg'}, {'end': 1196.968, 'src': 'embed', 'start': 1165.629, 'weight': 4, 'content': [{'end': 1167.15, 'text': 'Why though? Okay.', 'start': 1165.629, 'duration': 1.521}, {'end': 1170.052, 'text': "So now, so that's it.", 'start': 1167.27, 'duration': 2.782}, {'end': 1172.234, 'text': "So that's handling for X and Y.", 'start': 1170.232, 'duration': 2.002}, {'end': 1175.436, 'text': 'The problem here now becomes when it hits the wall.', 'start': 1172.234, 'duration': 3.202}, {'end': 1177.798, 'text': 'So recall our size is a 10.', 'start': 1175.536, 'duration': 2.262}, {'end': 1179.319, 'text': "We're trying to make a 10 by 10 grid.", 'start': 1177.798, 'duration': 1.521}, {'end': 1184.303, 'text': 'So if the agent attempts to move beyond that, we need to, we need to thwart it.', 'start': 1179.92, 'duration': 4.383}, {'end': 1185.424, 'text': 'Same thing with the food.', 'start': 1184.623, 'duration': 0.801}, {'end': 1186.865, 'text': 'If we allow the food to move.', 'start': 1185.684, 'duration': 1.181}, {'end': 1191.101, 'text': "spoiler alert, we'll turn on movement and you'll get to see it.", 'start': 1188.357, 'duration': 2.744}, {'end': 1193.624, 'text': "Or at least I'll show you a video of it.", 'start': 1192.262, 'duration': 1.362}, {'end': 1196.968, 'text': "It'll take probably too long to do it, but you can train it on your own.", 'start': 1193.944, 'duration': 3.024}], 'summary': 'Discussing the handling of x and y, size of a 10 by 10 grid, and movement restrictions for the agent and food.', 'duration': 31.339, 'max_score': 1165.629, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1165629.jpg'}, {'end': 1286.702, 'src': 'embed', 'start': 1249.401, 'weight': 3, 'content': [{'end': 1266.667, 'text': "okay, uh, now we're gonna do the exact same thing for y, so we'll say again y, and then we'll do y, y and y fabulous.", 'start': 1249.401, 'duration': 17.266}, {'end': 1278.278, 'text': "so now, uh, that should be everything we need for our blob class, which i will now refer to as blobjects, which you've all heard,", 'start': 1266.667, 'duration': 11.611}, {'end': 1280.22, 'text': "if you've seen any of my tutorials with blobs.", 'start': 1278.278, 'duration': 1.942}, {'end': 1286.702, 'text': 'So now what we want to do is either create the cue table or load the cue table.', 'start': 1280.88, 'duration': 5.822}], 'summary': 'Creating blobjects class for cue table or loading it.', 'duration': 37.301, 'max_score': 1249.401, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1249401.jpg'}], 'start': 974.159, 'title': 'Q-learning model success and handling movement for blobjects', 'summary': "Discusses the q-learning model's success in achieving almost 100% success rate in accessing food and coding the move method for blobjects within a 10x10 grid while ensuring boundaries are not surpassed.", 'chapters': [{'end': 1092.77, 'start': 974.159, 'title': 'Q-learning model success', 'summary': 'Discusses the success of a q-learning model in learning to use the wall in an environment, achieving almost 100% success rate in accessing the food despite the initial constraints.', 'duration': 118.611, 'highlights': ['The Q-learning model achieves almost 100% success rate in accessing the food despite initial constraints. The model can be trained for almost 100% success rate, succeeding almost 100% of the time despite initially inaccessible food locations.', "The model successfully learns to use the wall in the environment, which was unexpected. The model learns to use the wall in the environment, despite the creator's initial expectations, showcasing its unexpected learning capability.", "The creator is amazed by the model's ability to learn to use the wall. The creator expresses amazement at the model's ability to learn and utilize the wall effectively, showcasing the model's surprising learning capacity."]}, {'end': 1313.315, 'start': 1095.474, 'title': 'Handling movement and boundaries for blobjects', 'summary': 'Covers coding the move method for blobjects to allow random or specific movement within a 10x10 grid, with the code ensuring boundaries are not surpassed and the creation or loading of a cue table.', 'duration': 217.841, 'highlights': ['The chapter covers coding the move method for Blobjects to allow random or specific movement within a 10x10 grid, with the code ensuring boundaries are not surpassed and the creation or loading of a cue table', 'The code allows Blobjects to move randomly within the 10x10 grid, with the movement being limited to diagonal directions and the ability to move up or down', 'The chapter discusses the creation or loading of a cue table for Blobjects to aid in their movement and decision-making']}], 'duration': 339.156, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU974159.jpg', 'highlights': ['The Q-learning model achieves almost 100% success rate in accessing the food despite initial constraints.', 'The model successfully learns to use the wall in the environment, which was unexpected.', "The creator is amazed by the model's ability to learn to use the wall.", 'The chapter covers coding the move method for Blobjects to allow random or specific movement within a 10x10 grid, with the code ensuring boundaries are not surpassed and the creation or loading of a cue table.', 'The code allows Blobjects to move randomly within the 10x10 grid, with the movement being limited to diagonal directions and the ability to move up or down.', 'The chapter discusses the creation or loading of a cue table for Blobjects to aid in their movement and decision-making.']}, {'end': 1810.109, 'segs': [{'end': 1437.81, 'src': 'embed', 'start': 1375.508, 'weight': 1, 'content': [{'end': 1378.35, 'text': "right, we're going to subtract these two blobs from each other.", 'start': 1375.508, 'duration': 2.842}, {'end': 1387.257, 'text': "so where we overrode here, so it's going to be the subtraction of the play current player, of the player to the food.", 'start': 1378.35, 'duration': 8.907}, {'end': 1390.139, 'text': "so that's going to return one tuple of x and y values.", 'start': 1387.257, 'duration': 2.882}, {'end': 1396.486, 'text': 'And then the other one is going to be the difference or the subtraction or the delta of the player to the enemy.', 'start': 1390.7, 'duration': 5.786}, {'end': 1397.768, 'text': "So it'll be another tuple.", 'start': 1396.847, 'duration': 0.921}, {'end': 1399.63, 'text': "So that'll be X, Y.", 'start': 1398.248, 'duration': 1.382}, {'end': 1401.692, 'text': "So it'll be like X1, Y1, and then X2, Y2.", 'start': 1399.63, 'duration': 2.062}, {'end': 1406.89, 'text': "Okay It'll look like that.", 'start': 1401.932, 'duration': 4.958}, {'end': 1408.791, 'text': "That's our observation space.", 'start': 1407.25, 'duration': 1.541}, {'end': 1413.093, 'text': "Now, to get every combination, we're going to iterate through them.", 'start': 1409.531, 'duration': 3.562}, {'end': 1421.018, 'text': "So we've got I, I, I, I, I, I, I, I, I, I, I.", 'start': 1413.173, 'duration': 7.845}, {'end': 1423.6, 'text': 'Okay We could also get fancy.', 'start': 1421.018, 'duration': 2.582}, {'end': 1424.721, 'text': 'We could say X1, Y1, X2, X3.', 'start': 1423.66, 'duration': 1.061}, {'end': 1437.81, 'text': 'Y2 Okay, that might make a little more sense since I just showed that other example.', 'start': 1432.925, 'duration': 4.885}], 'summary': "Subtracting player's position from food and enemy to get x and y values, iterate through observation space for combinations.", 'duration': 62.302, 'max_score': 1375.508, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1375508.jpg'}, {'end': 1520.062, 'src': 'embed', 'start': 1494.193, 'weight': 2, 'content': [{'end': 1498.474, 'text': 'Okay So then now we just need to initialize with random values.', 'start': 1494.193, 'duration': 4.281}, {'end': 1501.575, 'text': "So we'll kind of, we'll just initialize the same way pretty much as before.", 'start': 1498.574, 'duration': 3.001}, {'end': 1512.879, 'text': "So that's going to be equal to and then, so basically each, each observation space needs four values.", 'start': 1501.675, 'duration': 11.204}, {'end': 1520.062, 'text': 'uh, four random values, because we have our action space is four.', 'start': 1514.519, 'duration': 5.543}], 'summary': 'Initializing observation space with four random values for action space of four.', 'duration': 25.869, 'max_score': 1494.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1494193.jpg'}, {'end': 1778.808, 'src': 'embed', 'start': 1753.21, 'weight': 0, 'content': [{'end': 1762.057, 'text': "um, so we're going to say obs equals and that's going to be player minus the food comma, player minus the enemy.", 'start': 1753.21, 'duration': 8.847}, {'end': 1764.839, 'text': "so that's our operator overloading in action.", 'start': 1762.057, 'duration': 2.782}, {'end': 1767.564, 'text': "Then we're going to have our random movement stuff.", 'start': 1765.663, 'duration': 1.901}, {'end': 1778.808, 'text': "So we're just going to say if np.random.random is greater than the epsilon, as long as that's the case, we're going to do a regular action.", 'start': 1767.584, 'duration': 11.224}], 'summary': 'Operator overloading in action, with random movement based on conditions.', 'duration': 25.598, 'max_score': 1753.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1753210.jpg'}], 'start': 1313.315, 'title': 'Game coordinates and q-table initialization', 'summary': 'Outlines coordinate calculation for player, food, and enemy in a game, involving shifting and subtracting coordinates to obtain two tuples of x and y values, and discusses initializing the q-table for reinforcement learning, including creating observation spaces, adding combinations, initializing with random values, loading pre-trained q-tables, and the training process involving episodes and actions.', 'chapters': [{'end': 1397.768, 'start': 1313.315, 'title': 'Coordinate calculation for player, food, and enemy', 'summary': 'Outlines the process of calculating coordinates for the player, food, and enemy in a game, emphasizing the need to shift and subtract coordinates, resulting in two tuples of x and y values, to be performed three times for observation space.', 'duration': 84.453, 'highlights': ['The chapter discusses the process of calculating coordinates for the player, food, and enemy, emphasizing the need to shift and subtract coordinates.', 'The process is iterated three times to obtain two tuples of x and y values for observation space.', 'The coordinates represent the relative difference between the player and the food, as well as the player and the enemy.']}, {'end': 1810.109, 'start': 1398.248, 'title': 'Reinforcement learning q-table initialization', 'summary': 'Discusses the process of initializing the q-table for reinforcement learning, including creating observation spaces, adding combinations to the table, and initializing with random values, along with the code for loading pre-trained q-tables and the training process involving episodes and actions.', 'duration': 411.861, 'highlights': ['The process of initializing the Q-Table involves creating observation spaces and adding every combination to the table. Observation spaces, adding combinations to the table', 'The Q-Table is initialized with random values, with each observation space requiring four random values, as the action space consists of four discrete actions. Initialization with random values, action space of four discrete actions', 'The code for loading pre-trained Q-Tables is provided, allowing for the loading of existing Q-Tables if available, or the creation of a new one if none exists. Loading pre-trained Q-Tables, creation of new Q-Table', 'The training process involves iterating through episodes, defining player, food, and enemy entities, and handling actions based on conditions such as epsilon and random movement. Training process, defining entities, handling actions']}], 'duration': 496.794, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1313315.jpg', 'highlights': ['The training process involves iterating through episodes, defining player, food, and enemy entities, and handling actions based on conditions such as epsilon and random movement.', 'The process of initializing the Q-Table involves creating observation spaces and adding every combination to the table.', 'The Q-Table is initialized with random values, with each observation space requiring four random values, as the action space consists of four discrete actions.', 'The chapter discusses the process of calculating coordinates for the player, food, and enemy, emphasizing the need to shift and subtract coordinates.']}, {'end': 2460.914, 'segs': [{'end': 1838.941, 'src': 'embed', 'start': 1810.109, 'weight': 3, 'content': [{'end': 1817.433, 'text': 'so we take that action which, um, based on the action passed, moves, based on these things that we pass.', 'start': 1810.109, 'duration': 7.324}, {'end': 1824.295, 'text': 'And again I just kind of wanted to separate these, because sometimes, like you, might want to move a thing, Maybe that thing,', 'start': 1819.033, 'duration': 5.262}, {'end': 1832.099, 'text': 'we want it to move more than one value for whatever reason, like the movement, and then the action to move Should, I think, should be separate?', 'start': 1824.295, 'duration': 7.804}, {'end': 1838.941, 'text': "For this simple example at the stage that we're at, maybe it's unnecessary, but I think it's good moving forward.", 'start': 1832.979, 'duration': 5.962}], 'summary': 'Discussing the need to separate actions for moving more than one value for better organization.', 'duration': 28.832, 'max_score': 1810.109, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1810109.jpg'}, {'end': 1913.63, 'src': 'embed', 'start': 1879.887, 'weight': 1, 'content': [{'end': 1882.67, 'text': "what about in training if the enemy can't move?", 'start': 1879.887, 'duration': 2.783}, {'end': 1887.814, 'text': "it's okay to even brush alongside the enemy, but if the enemy could move, you wouldn't want to take actions like that.", 'start': 1882.67, 'duration': 5.144}, {'end': 1889.616, 'text': "It'd be too dangerous because it could move into us.", 'start': 1887.834, 'duration': 1.782}, {'end': 1897.577, 'text': "so now what we're going to do is code um for the actual reward process.", 'start': 1891.132, 'duration': 6.445}, {'end': 1900.259, 'text': 'so, after we take our action, what was the result?', 'start': 1897.577, 'duration': 2.682}, {'end': 1913.63, 'text': "so what we're going to say is if player dot x equals enemy dot x and player dot y equals, uh, enemy dot y, you're done screwed up.", 'start': 1900.259, 'duration': 13.371}], 'summary': 'Training involves coding for reward process, avoiding dangerous actions, and ensuring enemy mobility.', 'duration': 33.743, 'max_score': 1879.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1879887.jpg'}, {'end': 2238.723, 'src': 'embed', 'start': 2156.238, 'weight': 0, 'content': [{'end': 2162.365, 'text': "Okay. so once we've got that, we want to update the cue table.", 'start': 2156.238, 'duration': 6.127}, {'end': 2171.276, 'text': "So we're going to say cue table at obs action equals whatever that new cue is.", 'start': 2162.405, 'duration': 8.871}, {'end': 2180.145, 'text': "okay. so at that point we're actually done with the q learning.", 'start': 2172.62, 'duration': 7.525}, {'end': 2182.706, 'text': 'now what we want to do is show the environment.', 'start': 2180.145, 'duration': 2.561}, {'end': 2183.687, 'text': 'if we want to show it,', 'start': 2182.706, 'duration': 0.981}, {'end': 2191.031, 'text': 'so because we kind of want to see it over time and then also we want to track some of the metrics and then maybe graph or whatever as needed.', 'start': 2183.687, 'duration': 7.344}, {'end': 2193.472, 'text': 'so q table obs actually is a new queue.', 'start': 2191.031, 'duration': 2.441}, {'end': 2198.095, 'text': 'so the next thing is, if show now, we want to show it now.', 'start': 2193.472, 'duration': 4.623}, {'end': 2200.918, 'text': "we haven't even created the environment, but it's just a grid.", 'start': 2198.095, 'duration': 2.823}, {'end': 2202.439, 'text': "so it's pretty simple environment.", 'start': 2200.918, 'duration': 1.521}, {'end': 2209.184, 'text': "so we're going to say the the base environment is equal to np dot zeros and it's a.", 'start': 2202.439, 'duration': 6.745}, {'end': 2210.465, 'text': 'actually this will be a tuple here.', 'start': 2209.184, 'duration': 1.281}, {'end': 2216.831, 'text': "uh, it's a size by size, so a 10 by 10 by 3, because it's rgb data.", 'start': 2210.465, 'duration': 6.366}, {'end': 2225.413, 'text': "and then we're going to say d type equals np dot u int 8..", 'start': 2216.831, 'duration': 8.582}, {'end': 2229.116, 'text': 'So this is just basically zero to two 56, zero to two 56 by three.', 'start': 2225.413, 'duration': 3.703}, {'end': 2232.078, 'text': "So let's fix that.", 'start': 2230.557, 'duration': 1.521}, {'end': 2238.723, 'text': "So now, uh, what we want to do is, so it's all zeros.", 'start': 2233.779, 'duration': 4.944}], 'summary': 'Update cue table, show environment, track metrics, create grid environment.', 'duration': 82.485, 'max_score': 2156.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2156238.jpg'}, {'end': 2383.208, 'src': 'embed', 'start': 2341.325, 'weight': 7, 'content': [{'end': 2343.307, 'text': 'So anyway, that would probably make more sense.', 'start': 2341.325, 'duration': 1.982}, {'end': 2345.789, 'text': 'but just show must go on.', 'start': 2343.307, 'duration': 2.482}, {'end': 2347.37, 'text': "so we've got food.", 'start': 2345.789, 'duration': 1.581}, {'end': 2348.391, 'text': 'Then we have the player.', 'start': 2347.37, 'duration': 1.021}, {'end': 2351.174, 'text': "so we're gonna say player, player, Why?", 'start': 2348.391, 'duration': 2.783}, {'end': 2366.447, 'text': 'player x is the player, player n, and then this will be the enemy and move this mouse, enemy n,', 'start': 2353.015, 'duration': 13.432}, {'end': 2376.196, 'text': 'and then here will be enemy dot y and then enemy dot x.', 'start': 2366.447, 'duration': 9.749}, {'end': 2383.208, 'text': "okay, so that'll change that grid's color, And by grid it's really an image now.", 'start': 2376.196, 'duration': 7.012}], 'summary': 'Discussion on game elements including players, enemies, and grid color change.', 'duration': 41.883, 'max_score': 2341.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2341325.jpg'}, {'end': 2460.914, 'src': 'embed', 'start': 2412.365, 'weight': 5, 'content': [{'end': 2421.428, 'text': "It's BGR and the way I found that out is by is yeah I let the player moves and the food and the enemy doesn't move and it's so clear.", 'start': 2412.365, 'duration': 9.063}, {'end': 2422.329, 'text': "It's BGR.", 'start': 2421.528, 'duration': 0.801}, {'end': 2425.49, 'text': "It's 100% BGR It's crazy.", 'start': 2422.349, 'duration': 3.141}, {'end': 2426.47, 'text': 'Anyway, not ENF.', 'start': 2425.67, 'duration': 0.8}, {'end': 2430.803, 'text': "It's ENV with a V.", 'start': 2427.131, 'duration': 3.672}, {'end': 2432.224, 'text': 'So that gives us our image.', 'start': 2430.803, 'duration': 1.421}, {'end': 2436.766, 'text': "Now what we're going to say is image equals image dot resize.", 'start': 2432.544, 'duration': 4.222}, {'end': 2440.087, 'text': "And then we're just going to resize this to, you can pick anything you want.", 'start': 2437.066, 'duration': 3.021}, {'end': 2441.888, 'text': "I'm going to say a 300 by 300, just so it's like, I can see it.", 'start': 2440.107, 'duration': 1.781}, {'end': 2447.039, 'text': 'and then cv2.', 'start': 2445.689, 'duration': 1.35}, {'end': 2458.754, 'text': "m show and then the title is whatever you want it to be numpy array image and then i'm going to throw in some kind of shoddy code here.", 'start': 2447.039, 'duration': 11.715}, {'end': 2459.554, 'text': "but that's okay.", 'start': 2458.754, 'duration': 0.8}, {'end': 2460.914, 'text': 'everybody accepts it.', 'start': 2459.554, 'duration': 1.36}], 'summary': 'Game analysis: bgr format, 100% bgr, env with v, image resizing to 300x300', 'duration': 48.549, 'max_score': 2412.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2412365.jpg'}], 'start': 1810.109, 'title': 'Implementing reinforcement learning and image manipulation', 'summary': 'Covers the implementation of a q-learning algorithm for environment navigation, including the process of taking actions, calculating rewards, updating the q-table, and visualizing the environment. it also focuses on creating an rgb image of size 10x10x3, initializing the environment with all black pixels, resizing the image to 300x300 for visualization using numpy and opencv, and mentions the conversion of rgb to bgr format for image display.', 'chapters': [{'end': 2202.439, 'start': 1810.109, 'title': 'Reinforcement learning for environment navigation', 'summary': 'Covers the implementation of a q-learning algorithm for environment navigation, including the process of taking actions, calculating rewards, updating the q-table, and visualizing the environment.', 'duration': 392.33, 'highlights': ['Implementation of Q-learning algorithm for environment navigation The chapter focuses on implementing a Q-learning algorithm for environment navigation, demonstrating the process of taking actions, calculating rewards, updating the Q-table, and visualizing the environment.', 'Separating actions for moving entities The discussion includes the consideration of separating actions for moving entities, emphasizing the need for distinct actions for different entities and the potential complexity it introduces to the environment.', 'Calculation of rewards and updating Q-table The process of calculating rewards based on player and enemy positions, the handling of different reward scenarios (food reward, enemy penalty, negative move penalty), and the subsequent updating of the Q-table are explained in detail.', 'Visualization and tracking of environment metrics The chapter also addresses the visualization of the environment over time and the tracking of metrics for potential graphing and analysis, indicating the comprehensive approach to monitoring the learning process.']}, {'end': 2460.914, 'start': 2202.439, 'title': 'Creating rgb image and resizing', 'summary': 'Focuses on creating an rgb image of size 10x10x3, initializing the environment with all black pixels, and resizing the image to 300x300 for visualization using numpy and opencv. it also mentions the conversion of rgb to bgr format for image display.', 'duration': 258.475, 'highlights': ['The environment is initialized as a 10x10x3 RGB image with all black pixels using numpy, ensuring a total of 300 pixels (10x10) and 3 color channels.', 'The RGB image is converted to BGR format for visualization using OpenCV, indicating the need for this specific format for correct display.', 'The image is resized to 300x300 for visualization using cv2.resize, allowing a clearer view of the environment.', 'The player, food, and enemy positions are defined within the 10x10 grid, highlighting the spatial attributes of the environment.']}], 'duration': 650.805, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU1810109.jpg', 'highlights': ['Implementation of Q-learning algorithm for environment navigation, including actions, rewards, Q-table update, and visualization.', 'Calculation of rewards based on player and enemy positions, handling different reward scenarios, and updating the Q-table.', 'Visualization and tracking of environment metrics for graphing and analysis.', 'Separation of actions for moving entities and its impact on environment complexity.', 'Initialization of environment as a 10x10x3 RGB image with all black pixels using numpy.', 'Conversion of RGB to BGR format for correct visualization using OpenCV.', 'Resizing the image to 300x300 for clearer environment visualization.', 'Defining player, food, and enemy positions within the 10x10 grid.']}, {'end': 3315.346, 'segs': [{'end': 2491.177, 'src': 'embed', 'start': 2460.914, 'weight': 3, 'content': [{'end': 2476.645, 'text': 'if reward is equal to the food reward or the reward is equal to a negative enemy penalty, this means the simulation ended and we we really screwed up.', 'start': 2460.914, 'duration': 15.731}, {'end': 2483.77, 'text': "so um, so not if we didn't get the food, but if we got the food, No, we didn't screw up necessarily.", 'start': 2476.645, 'duration': 7.125}, {'end': 2487.133, 'text': 'If we got the food, yay, or we hit the enemy really bad.', 'start': 2483.83, 'duration': 3.303}, {'end': 2491.177, 'text': "So, if that's the case, I just wanted to pause it just for a moment longer.", 'start': 2487.634, 'duration': 3.543}], 'summary': 'Simulation ends if reward equals food or negative enemy penalty, indicating failure or success.', 'duration': 30.263, 'max_score': 2460.914, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2460914.jpg'}, {'end': 2657.659, 'src': 'embed', 'start': 2618.763, 'weight': 4, 'content': [{'end': 2623.466, 'text': 'And then finally, uh, we just want to graph things at the very end.', 'start': 2618.763, 'duration': 4.703}, {'end': 2632.753, 'text': "So I'm going to say moving underscore average is equal to the NumPy convolve, uh, episode rewards.", 'start': 2623.626, 'duration': 9.127}, {'end': 2640.119, 'text': 'So this will just create a moving average and NumPy ones, uh, show every.', 'start': 2632.813, 'duration': 7.306}, {'end': 2646.475, 'text': 'And then divide by show every.', 'start': 2642.034, 'duration': 4.441}, {'end': 2653.618, 'text': "And then, is this mode? I don't even know.", 'start': 2646.495, 'duration': 7.123}, {'end': 2655.138, 'text': "Let's tab that over.", 'start': 2654.258, 'duration': 0.88}, {'end': 2657.659, 'text': 'Yeah, mode is valid.', 'start': 2656.059, 'duration': 1.6}], 'summary': 'Creating a moving average of the episode rewards using numpy convolve.', 'duration': 38.896, 'max_score': 2618.763, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2618763.jpg'}, {'end': 2790.334, 'src': 'heatmap', 'start': 2751.807, 'weight': 1, 'content': [{'end': 2768.697, 'text': "dot pickle with open that pickle, comma wb as f pickle dot dump and we'll dump our queue table into f.", 'start': 2751.807, 'duration': 16.89}, {'end': 2771.338, 'text': 'whoo, okay, coded that all the way through.', 'start': 2768.697, 'duration': 2.641}, {'end': 2775.881, 'text': 'how many reward on food reward?', 'start': 2771.338, 'duration': 4.543}, {'end': 2780.322, 'text': 'oh so, this should be lowercase reward, lowercase reward.', 'start': 2775.881, 'duration': 4.441}, {'end': 2790.334, 'text': "So what are the odds that I coded this without any other errors? I don't know.", 'start': 2780.602, 'duration': 9.732}], 'summary': 'Code successfully executed with no apparent errors', 'duration': 38.527, 'max_score': 2751.807, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2751807.jpg'}, {'end': 2924.835, 'src': 'embed', 'start': 2900.756, 'weight': 5, 'content': [{'end': 2909.183, 'text': 'um cool, so you can see it definitely learned things really quickly and again half of the time it should not be able to get there,', 'start': 2900.756, 'duration': 8.427}, {'end': 2912.666, 'text': 'but it uses the wall to get there, which is super cool.', 'start': 2909.183, 'duration': 3.483}, {'end': 2916.889, 'text': "um okay, so what i'm gonna do is close out of here.", 'start': 2912.666, 'duration': 4.223}, {'end': 2920.112, 'text': "i guess i'll just uh, Oh oh, I had to save.", 'start': 2916.889, 'duration': 3.223}, {'end': 2921.513, 'text': 'Okay, so it saves our cue table.', 'start': 2920.172, 'duration': 1.341}, {'end': 2924.835, 'text': 'So as you can see, our cue table is like 13.6 megabytes.', 'start': 2922.113, 'duration': 2.722}], 'summary': 'The ai learned quickly and used the wall to reach its goal, with a cue table size of 13.6 megabytes.', 'duration': 24.079, 'max_score': 2900.756, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2900756.jpg'}, {'end': 3150.618, 'src': 'embed', 'start': 3118.689, 'weight': 0, 'content': [{'end': 3130.458, 'text': 'The next thing, or the only other thing I want to show you guys, is some of the results of using much, much larger environment.', 'start': 3118.689, 'duration': 11.769}, {'end': 3133.201, 'text': "so in so we're doing a 10 by 10.", 'start': 3130.458, 'duration': 2.743}, {'end': 3144.332, 'text': "if you just change that to a 20 by 20, your cue table size goes from about 15 megabytes to like 250 megabytes, Just for comparison's sake.", 'start': 3133.201, 'duration': 11.131}, {'end': 3147.936, 'text': 'And then if I figure out where the heck.', 'start': 3144.773, 'duration': 3.163}, {'end': 3150.618, 'text': "I promise you, there's some cool videos and photos.", 'start': 3147.936, 'duration': 2.682}], 'summary': 'Using a larger environment (20x20) increases cue table size from 15mb to 250mb.', 'duration': 31.929, 'max_score': 3118.689, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU3118689.jpg'}, {'end': 3217.735, 'src': 'embed', 'start': 3194.612, 'weight': 1, 'content': [{'end': 3202.32, 'text': "um, and i almost wonder if allowing movement makes it easier, because he's always trying to get toward he, He's always trying to get towards the food,", 'start': 3194.612, 'duration': 7.708}, {'end': 3208.13, 'text': 'and since the food is capable of moving in ones like up down, left, right, not just diagonally,', 'start': 3202.32, 'duration': 5.81}, {'end': 3212.538, 'text': "that that might actually even make it easier for the agent, let's say rather than he.", 'start': 3208.13, 'duration': 4.408}, {'end': 3216.814, 'text': "Okay, so that's our environment.", 'start': 3214.853, 'duration': 1.961}, {'end': 3217.735, 'text': 'I hope you guys enjoyed.', 'start': 3216.834, 'duration': 0.901}], 'summary': 'Agent may find it easier to reach food in a dynamic environment.', 'duration': 23.123, 'max_score': 3194.612, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU3194612.jpg'}], 'start': 2460.914, 'title': 'Q learning and reinforcement analysis', 'summary': "Covers q learning simulation process, ending conditions, code elements, reward visualization, and qtable analysis demonstrating the agent's performance in different scenarios, with a notable cue table size increase when transitioning to a 20 by 20 environment.", 'chapters': [{'end': 2953.213, 'start': 2460.914, 'title': 'Q learning simulation', 'summary': 'Explains the q learning simulation process, including the conditions for ending the simulation, the key elements of the code, the visualization of the moving average of rewards, and the expansion of the cue table over time.', 'duration': 492.299, 'highlights': ['The simulation ends if the reward equals the food reward or the negative enemy penalty, with the pause set at 500 milliseconds for user interaction.', 'The code includes the accumulation of episode rewards, the decay of epsilon, and the visualization of the moving average of rewards using NumPy convolve and matplotlib.', 'The cue table is saved as a pickle file, with a size of 13.6 megabytes, and the expansion over time can indicate the updating of random initialized values.']}, {'end': 3315.346, 'start': 2953.213, 'title': 'Reinforcement learning qtable analysis', 'summary': "Explores the analysis of an environment using qtable learning, demonstrating the agent's learning behavior and performance in different scenarios, with a notable increase in the cue table size when transitioning from a 10 by 10 to a 20 by 20 environment.", 'duration': 362.133, 'highlights': ['The agent demonstrates successful learning behavior in navigating a 20 by 20 environment with no movement and still performs well when movement is introduced for both the enemy and the food. Successful learning behavior demonstrated in a 20 by 20 environment with and without movement for both enemy and food.', 'The cue table size increases from about 15 megabytes to approximately 250 megabytes when transitioning from a 10 by 10 to a 20 by 20 environment. Significant increase in cue table size from 10 by 10 to 20 by 20 environment.', "The environment's complexity is evident in the agent's ability to navigate around the enemy and adapt to the moving food, raising the question of whether allowing movement makes it easier for the agent to achieve its goal. Agent's ability to navigate around the enemy and adapt to moving food indicates environment complexity."]}], 'duration': 854.432, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/G92TF4xYQcU/pics/G92TF4xYQcU2460914.jpg', 'highlights': ['The cue table size increases from about 15 megabytes to approximately 250 megabytes when transitioning from a 10 by 10 to a 20 by 20 environment. Significant increase in cue table size from 10 by 10 to 20 by 20 environment.', 'The agent demonstrates successful learning behavior in navigating a 20 by 20 environment with no movement and still performs well when movement is introduced for both the enemy and the food. Successful learning behavior demonstrated in a 20 by 20 environment with and without movement for both enemy and food.', "The environment's complexity is evident in the agent's ability to navigate around the enemy and adapt to the moving food, raising the question of whether allowing movement makes it easier for the agent to achieve its goal. Agent's ability to navigate around the enemy and adapt to moving food indicates environment complexity.", 'The simulation ends if the reward equals the food reward or the negative enemy penalty, with the pause set at 500 milliseconds for user interaction.', 'The code includes the accumulation of episode rewards, the decay of epsilon, and the visualization of the moving average of rewards using NumPy convolve and matplotlib.', 'The cue table is saved as a pickle file, with a size of 13.6 megabytes, and the expansion over time can indicate the updating of random initialized values.']}], 'highlights': ['The Q-learning model achieves almost 100% success rate in accessing the food despite initial constraints.', 'The agent demonstrates successful learning behavior in navigating a 20 by 20 environment with no movement and still performs well when movement is introduced for both the enemy and the food. Successful learning behavior demonstrated in a 20 by 20 environment with and without movement for both enemy and food.', "The environment's complexity is evident in the agent's ability to navigate around the enemy and adapt to the moving food, raising the question of whether allowing movement makes it easier for the agent to achieve its goal. Agent's ability to navigate around the enemy and adapt to moving food indicates environment complexity.", 'The cue table size increases from about 15 megabytes to approximately 250 megabytes when transitioning from a 10 by 10 to a 20 by 20 environment. Significant increase in cue table size from 10 by 10 to 20 by 20 environment.', 'The training process involves iterating through episodes, defining player, food, and enemy entities, and handling actions based on conditions such as epsilon and random movement.', 'The chapter introduces building a custom Q-Learning environment with player, food, and enemy blobs.', 'Importing necessary modules and setting up the environment for OpenCV, NumPy, and Python Imaging Library.', 'The blobs in the game simulation require a blob class with shared attributes such as the ability to move and a starting location, aiming for efficient implementation (e.g., minimizing observation space complexity).', 'Implementation of Q-learning algorithm for environment navigation, including actions, rewards, Q-table update, and visualization.', 'The method initialization involves defining the init method for initializing the x and y coordinates randomly using numpy.random.randint.', 'The code includes the accumulation of episode rewards, the decay of epsilon, and the visualization of the moving average of rewards using NumPy convolve and matplotlib.']}