The code to my question is as below, for reference:
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,...
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 ...
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 ...