mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-17 21:20:11 +00:00
9003a09d5b
* callbacks have access to locals * changeloc * doc * callbacks have access to locals * changeloc * doc * Added update function for child callbacks * Pre-Release 0.8.0 (#134) * Fix double reset and improve typing coverage (#136) * Fix double reset and improve typing coverage * Revert minor edit * Add doc about types * Update child callbacks * cleaned imports * format * import order * Simplify tests and add comments Co-authored-by: Antonin RAFFIN <antonin.raffin@ensta.org>
65 lines
2.3 KiB
Python
65 lines
2.3 KiB
Python
import os
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import shutil
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import gym
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import pytest
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from stable_baselines3 import A2C, DDPG, DQN, PPO, SAC, TD3
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from stable_baselines3.common.callbacks import (
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CallbackList,
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CheckpointCallback,
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EvalCallback,
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EveryNTimesteps,
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StopTrainingOnRewardThreshold,
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)
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@pytest.mark.parametrize("model_class", [A2C, PPO, SAC, TD3, DQN, DDPG])
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def test_callbacks(tmp_path, model_class):
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log_folder = tmp_path / "logs/callbacks/"
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# Dyn only support discrete actions
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env_name = select_env(model_class)
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# Create RL model
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# Small network for fast test
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model = model_class("MlpPolicy", env_name, policy_kwargs=dict(net_arch=[32]))
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checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_folder)
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eval_env = gym.make(env_name)
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# Stop training if the performance is good enough
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callback_on_best = StopTrainingOnRewardThreshold(reward_threshold=-1200, verbose=1)
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eval_callback = EvalCallback(
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eval_env, callback_on_new_best=callback_on_best, best_model_save_path=log_folder, log_path=log_folder, eval_freq=100
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)
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# Equivalent to the `checkpoint_callback`
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# but here in an event-driven manner
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checkpoint_on_event = CheckpointCallback(save_freq=1, save_path=log_folder, name_prefix="event")
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event_callback = EveryNTimesteps(n_steps=500, callback=checkpoint_on_event)
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callback = CallbackList([checkpoint_callback, eval_callback, event_callback])
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model.learn(500, callback=callback)
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# Check access to local variables
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assert model.env.observation_space.contains(callback.locals["new_obs"][0])
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# Check that the child callback was called
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assert checkpoint_callback.locals["new_obs"] is callback.locals["new_obs"]
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assert event_callback.locals["new_obs"] is callback.locals["new_obs"]
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assert checkpoint_on_event.locals["new_obs"] is callback.locals["new_obs"]
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model.learn(500, callback=None)
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# Transform callback into a callback list automatically
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model.learn(500, callback=[checkpoint_callback, eval_callback])
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# Automatic wrapping, old way of doing callbacks
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model.learn(500, callback=lambda _locals, _globals: True)
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if os.path.exists(log_folder):
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shutil.rmtree(log_folder)
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def select_env(model_class) -> str:
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if model_class is DQN:
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return "CartPole-v0"
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else:
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return "Pendulum-v0"
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