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High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
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703
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Use the training `end_e` as the `evaluation(..., epsilon=end_e)` (#430)
a00e71bView on GitHubFix: PQN needs to mask the return computation if next state is done (#494)
1ed8062View on GitHubremoving the unused noise_clip from sac_continuous_actions.py (probably a relic from TD3) (#456)
65789baView on GitHub