mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-14 20:58:03 +00:00
5ff176b2f1
* Add DDPG + TD3 with any number of critics * Allow any number of critics for SAC * Update doc * [ci skip] Update DDPG example * Remove unused parameter * Add DDPG to identity test * Fix computation with n_critics=1,3 * Update doc * Apply suggestions from code review Co-authored-by: Adam Gleave <adam@gleave.me> * Update docstrings for off-policy algos * Add check for sde Co-authored-by: Adam Gleave <adam@gleave.me>
54 lines
2 KiB
Python
54 lines
2 KiB
Python
import os
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import shutil
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import pytest
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import gym
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from stable_baselines3 import A2C, PPO, SAC, TD3, DQN, DDPG
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from stable_baselines3.common.callbacks import (CallbackList, CheckpointCallback, EvalCallback,
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EveryNTimesteps, StopTrainingOnRewardThreshold)
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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(eval_env, callback_on_new_best=callback_on_best,
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best_model_save_path=log_folder,
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log_path=log_folder, eval_freq=100)
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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,
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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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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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