mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-05-17 21:20:11 +00:00
49 lines
1.9 KiB
Python
49 lines
1.9 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 torchy_baselines import A2C, CEMRL, PPO, SAC, TD3
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from torchy_baselines.common.callbacks import (CallbackList, CheckpointCallback, EvalCallback,
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EveryNTimesteps, StopTrainingOnRewardThreshold)
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@pytest.mark.parametrize("model_class", [A2C, CEMRL, PPO, SAC, TD3])
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def test_callbacks(model_class):
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log_folder = './logs/callbacks/'
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kwargs = {}
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if model_class == CEMRL:
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kwargs['pop_size'] = 2
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kwargs['n_grad'] = 1
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# Create RL model
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# Small network for fast test
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model = model_class('MlpPolicy', 'Pendulum-v0', policy_kwargs=dict(net_arch=[32]), **kwargs)
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checkpoint_callback = CheckpointCallback(save_freq=1000, save_path=log_folder)
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eval_env = gym.make('Pendulum-v0')
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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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