My main purpose right now is to train an agent using the A2C algorithm to solve the Atari Breakout game. So far I have succeeded to create that code with a single agent and environment. To break the correlation between samples (i.i.d), I need to have an agent interacting with several environments.
class GymEnvVec(): def __init__(self, env_name, n_envs, seed=0): make_env = lambda: gym.make(env_name) self.envs = [make_env() for _ in range(n_envs)] [env.seed(seed + 10 * i) for i, env in enumerate(self.envs)] def reset(self): return [env.reset() for env in self.envs] def step(self, actions): return list(zip(*[env.step(a) for env, a in zip(self.envs, actions)]))
I can use the class
GymEnvVec to vectorize my environment.
So I can set my environments with
envs = GymEnvVec(env_name="Breakout-v0", n_envs=50)
I can get my first observations with
observations = envs.reset()
Pick some actions with
actions = agent.choose_actions(observations)
choose_actions method might look like
def choose_actions(self, states): assert isinstance(states, (list, tuple)) actions =  for state in states: probabilities = F.softmax(self.network(state)) action_probs = T.distributions.Categorical(probabilities) actions.append(action_probs.sample()) return [action.item() for action in actions]
Finally, the environments will spit the next_states, rewards and if it is done with
next_states, rewards, dones, _ = env.step(actions)
It is at this point I am a bit confused. I think I need to gather immediate experiences, batch altogether and forward it to the agent. My problem is probably with the "gather immediate experiences".
I propose a solution, but I am far from being sure it is a good answer. At each iteration, I think I must take a random number with
nb = random.randint(0, len(n_envs)-1)
and put the experience in history with
history.append(Experience(state=states[nb], actions[nb], rewards[nb], dones[nb]))
Am I wrong? Can you tell me what I should do?