Reinforcement Learning: Exploration, Robustness, and Safety

The field of reinforcement learning is moving towards more robust and efficient methods for exploration, policy optimization, and safety. Recent developments highlight the importance of balancing reward design and entropy maximization in complex control tasks, as well as the need for more effective exploration strategies. The use of uncertainty estimation and prioritized experience replay is also gaining attention as a means to improve sample efficiency and reduce the impact of noise in value estimation. Furthermore, researchers are investigating the vulnerability of deep reinforcement learning agents to environmental state perturbations and backdoor attacks, with a focus on developing more robust and secure methods. Noteworthy papers in this area include:

  • 'Exploration by Random Reward Perturbation', which introduces a novel exploration strategy that enhances policy diversity during training.
  • 'TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning', which systematically optimizes backdoor triggers for deep reinforcement learning algorithms.
  • 'Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization', which analyzes the interplay between entropy regularization and constraints penalization to achieve robust safety in reinforcement learning.

Sources

When Maximum Entropy Misleads Policy Optimization

Learning Deterministic Policies with Policy Gradients in Constrained Markov Decision Processes

Exploration by Random Reward Perturbation

Towards Robust Deep Reinforcement Learning against Environmental State Perturbation

Uncertainty Prioritized Experience Replay

TooBadRL: Trigger Optimization to Boost Effectiveness of Backdoor Attacks on Deep Reinforcement Learning

Viability of Future Actions: Robust Safety in Reinforcement Learning via Entropy Regularization

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