The field of offline reinforcement learning is moving towards developing more effective and efficient methods for learning policies from fixed datasets. Recent work has focused on addressing the challenges of reward shaping, exploration, and generalization in offline reinforcement learning. One notable trend is the incorporation of physics-informed inductive biases and symbolic programming to improve sample efficiency and generalization. Another area of research is the development of innovative frameworks and algorithms that can effectively leverage prior knowledge and uncertainty to improve policy learning. Noteworthy papers in this area include:

• TROFI, which proposes a novel approach to offline inverse reinforcement learning without a pre-defined reward function.
• R3S, which presents an innovative offline RL framework that integrates inherent model uncertainty to tackle intrinsic fluctuations in reward predictions.
• PiPRL, which develops a physics-informed program-guided RL framework for indoor navigation.
• RRM, which proposes a method to effectively leverage prior knowledge with a Residual Reward Model for preference-based reinforcement learning.
• DSAC-D, which proposes a distributional reinforcement learning algorithm that can converge to the optimal policy by introducing policy entropy and value distribution function.
• PANI, which proposes a method that simply enhances offline learning by utilizing noise-injected actions to cover the entire action space, while penalizing according to the amount of noise injected.