New Optimistic Policy Regularization boosts RL agent performance on Atari games

Researchers have introduced Optimistic Policy Regularization (OPR), a novel method to enhance reinforcement learning agents by incorporating rare, high-return episodes as a reward bonus. This technique, a simple modification to Proximal Policy Optimization (PPO), does not require auxiliary networks or complex loops. In experiments on 49 Atari games, OPR achieved a median human-normalized score of 148.4% with significantly fewer decisions than baseline methods, outperforming published PPO results on most games. AI

IMPACT This method could lead to more efficient training of reinforcement learning agents, particularly in environments with sparse rewards.

RANK_REASON The cluster contains a research paper detailing a new method for reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Atari
• Generalized Advantage Estimation
• Hugging Face
• human-normalized score
• Mai T Pham
• Optimistic Policy Regularization
• Proximal Policy Optimization
• Self-Imitation Learning via Generalized Lower Bound Q-learning

• paper
• other

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