The recent developments in the field of reinforcement learning (RL) and inverse reinforcement learning (IRL) have been focusing on enhancing the safety, efficiency, and adaptability of learning algorithms in complex and real-world scenarios. A significant trend is the emphasis on leveraging offline data to improve online learning processes, ensuring that agents can explore and learn without causing harm. This includes innovative approaches to unsupervised data collection and the development of novel algorithms that can learn optimal policies from limited or suboptimal demonstrations. Another key direction is the refinement of reward learning frameworks, where new quantitative methods are being introduced to better identify and utilize rewards that lead to improved performance. Additionally, there's a growing interest in transfer learning for policy adaptation, particularly under conditions of covariate shift, aiming to learn optimal policies in target domains with minimal data. These advancements collectively aim to make RL and IRL more practical and effective for a wide range of applications.

Noteworthy Papers

• Safe Reinforcement Learning with Minimal Supervision: Introduces an unsupervised RL-based offline data collection method and optimistic forgetting, enhancing safe online exploration with limited data.
• On the Partial Identifiability in Reward Learning: Presents a quantitative framework for analyzing reward learning problems, offering new algorithms for reward transfer scenarios.
• Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data: Develops GILD, a flexible module for off-policy RL algorithms that meta-learns objectives from offline data, significantly outperforming state-of-the-art methods.
• Reward Compatibility: A Framework for Inverse RL: Introduces a novel framework for quantifying reward compatibility, extending the realm of provably efficient IRL to large-scale MDPs.
• Optimal Policy Adaptation under Covariate Shift: Proposes a principled approach for learning optimal policies in target domains under covariate shift, demonstrating accurate reward estimation and policy approximation.