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About this paper
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su, Yi-Hsiang Chang, Chun-Yi Lee
Researchers at National Tsing Hua University developed a diversity-driven exploration strategy for Deep Reinforcement Learning that modifies the standard loss function to encourage the current policy to diverge from recent past policies. This approach significantly improves exploration efficiency and performance across challenging environments characterized by sparse or deceptive rewards and large state spaces, outperforming baselines in various Atari and MuJoCo tasks.
Key insights
- Explicitly encouraging policy divergence provides a systematic and proactive exploration mechanism that helps agents escape local optima and explore more broadly.
- A simple regularization term integrated into existing DRL loss functions can be highly effective and broadly applicable to both on-policy and off-policy algorithms.
- Dynamically adjusting the strength of the diversity incentive through adaptive scaling improves the robustness and overall performance of the exploration strategy.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.