I’m trying to understand how to run multiple gym environments at the same time using Ray. Here’s some code I found:
import gym
import ray
@ray.remote
class GameEnv(object):
def __init__(self):
self.environment = gym.make("Breakout-v0")
self.environment.reset()
def take_action(self, move):
return self.environment.step(move)
# Initialize the remote environment
game_sim = GameEnv.remote()
results = []
for i in range(3):
# Execute action 1 in the environment
results.append(game_sim.take_action.remote(1))
I’m confused about whether this actually runs in parallel or not. Since there’s only one environment instance, wouldn’t the actions still execute one after another? If the actions are still sequential, then what benefit does this approach provide over regular synchronous execution? Can someone explain how this parallelization works with gym environments?
u nailed it! with only one instance, the actions are just going to queue up. to achieve real parallelism, create multiple GameEnv instances like this: envs = [GameEnv.remote() for _ in range(4)]. then u can run actions in parallel and that’ll show Ray’s true power with gym environments!
You’re correct that the provided code only creates one remote environment, meaning the actions are executed sequentially. The misunderstanding lies in what is actually being parallelized. When you invoke game_sim.take_action.remote(1) multiple times, you are queuing tasks onto the same remote actor, which handles them one at a time.
This structure is beneficial when you’re looking to manage different environments together or distribute tasks across multiple workers. To achieve real parallel execution, you must create multiple instances of the environment (as Laura pointed out) or utilize Ray’s capabilities for handling independent simulation runs.
At present, this design simply encapsulates the environment in its own process. While this is useful for memory management and improves fault tolerance, it does not facilitate the level of parallelism you’re aiming for.