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The code to my question is as below, for reference: import gym import numpy as np import matplotlib.pyplot as plt # Discretize the contiuous space DISCRETE_POINTS = 50 X_position = np.linspace(-2.4, 2.4, DISCRETE_POINTS) Velocity = np.linspace(-5, 5, DISCRETE_POINTS) Angle = np.linspace(-0.7295476, 0.7295476, DISCRETE_POINTS) Angular_vel = np.linspace(-5,5,...


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Tl;dr max-pool You can see in the diagram, everywhere there are a variable number of inputs (pickups, units, hero modifiers/abilities/items), a max-pool follows, though I don't know the specifics of the max-pool implementation. From https://neuro.cs.ut.ee/the-use-of-embeddings-in-openai-five : Notice that while the number of modifiers, abilities and items ...


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What I was looking for is multi-agent RL, where I have multiple RL agents, each controlling actions of one user. All RL agents/user make an action in each environment step and each get their own reward. I represent my RL agents' actions as dict, containing the RL agent ID as key and its action as value. The different agents may either use the same or a ...


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