gym example demonstrates the AEA framework's flexibility with respect to Reinforcement Learning using OpenAI's
There is no immediate use case for this example as you can train an RL agent without the AEA proxy layer just fine (and faster).
However, the example decouples the RL agent from the
gym.Env allowing them to run in separate execution environments, potentially owned by different entities.
pip install numpy gym
Run the example
Notice the usual RL setup, i.e. the fit method of the RL agent has the typical signature and a familiar implementation.
train.py demonstrates how easy it is to use an AEA agent as a proxy layer between an OpenAI
gym.Env and a standard RL agent.
It is just one line of code to introduce the proxy agent and proxy environment!
from gyms.env import BanditNArmedRandom from proxy.env import ProxyEnv from rl.agent import RLAgent if __name__ == "__main__": NB_GOODS = 10 NB_PRICES_PER_GOOD = 100 NB_STEPS = 4000 # Use any gym.Env compatible environment: gym_env = BanditNArmedRandom(nb_bandits=NB_GOODS, nb_prices_per_bandit=NB_PRICES_PER_GOOD) # Pass the gym environment to a proxy environment: proxy_env = ProxyEnv(gym_env) # Use any RL agent compatible with the gym environment and call the fit method: rl_agent = RLAgent(nb_goods=NB_GOODS) rl_agent.fit(env=proxy_env, nb_steps=NB_STEPS)