Async Gym Agents
Drop-in asynchronous data collection for Stable Baselines 3 agents.
Known framework callbacks are processed from complete episode batches when their timing permits. Off-policy checkpointing and unrecognized Stable Baselines callbacks retain their normal per-step behavior.
Usage
from functools import partial
import gymnasium as gym
from stable_baselines3 import TD3
from async_gym_agents.agents.async_agent import get_injected_agent
from async_gym_agents.envs.multi_env import IndexableMultiEnv
env = IndexableMultiEnv(
[partial(gym.make, "Pendulum-v1") for _ in range(8)]
)
model = get_injected_agent(TD3)(
"MlpPolicy",
env,
use_mp=False,
max_episodes_in_buffer=8,
)
model.learn(total_timesteps=10)
model.shutdown()
Workers send complete episodes, so on-policy rollouts may exceed n_steps by
the final episode. max_episodes_in_buffer limits buffered episodes across all
workers. queue_put_timeout defaults to None; set a finite timeout to allow
episode drops instead of waiting for buffer capacity.
Episodes are decoded in a background assembler. On-policy agents prepare the next rollout buffer, while off-policy agents prepare one episode ahead and keep replay-buffer insertion on the trainer thread.
On-policy episode packets retain both the environment reward and the time-limit-bootstrapped training reward. Stable Baselines callbacks, batched logging, and pruning receive the environment reward; rollout-buffer returns and advantages use the training reward. Off-policy episodes retain their existing single reward view.
Use get_profiler_report() to inspect trainer, worker, buffer, transport, and
policy statistics. transport.utilization is the current fraction of bounded
episode slots in use. Episode-send backpressure is reported once as
buffer.avg_push_wait_seconds; the buffer section does not contain historical
queue utilization or emptiness estimates.
2 maintainers
Metadata
2 maintainers
Release files for async-gym-agents 0.5.0
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