The field of reinforcement learning is moving towards addressing complex, high-dimensional environments with partial observability. Researchers are exploring innovative methods to improve policy optimization, value estimation, and representation learning in such environments. Notable trends include the integration of causal reasoning, predictive coding, and variational inference to enhance the robustness and interpretability of reinforcement learning agents. Additionally, there is a growing interest in developing algorithms that can handle non-stationary and reward-sparse environments, with applications in areas like autonomous underwater vehicles. Some noteworthy papers in this regard include 'Confounding Robust Deep Reinforcement Learning: A Causal Approach', which proposes a novel algorithm for off-policy learning in the presence of unobserved confounding, and 'Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability', which demonstrates the effectiveness of integrating predictive coding modules into meta-reinforcement learning. Overall, the field is advancing towards more robust, efficient, and interpretable reinforcement learning methods that can tackle complex real-world problems.
Advances in Reinforcement Learning and Partially Observable Environments
Sources
Hardness of Approximation for Shortest Path with Vector Costs
On the Sample Complexity of Differentially Private Policy Optimization
ESCORT: Efficient Stein-variational and Sliced Consistency-Optimized Temporal Belief Representation for POMDPs
Confounding Robust Deep Reinforcement Learning: A Causal Approach
How Hard is it to Confuse a World Model?
Surrogate-based quantification of policy uncertainty in generative flow networks
Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
Is Temporal Difference Learning the Gold Standard for Stitching in RL?
Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
Transitive RL: Value Learning via Divide and Conquer
Variational Polya Tree
FlowCritic: Bridging Value Estimation with Flow Matching in Reinforcement Learning
Causal Deep Q Network
An Information-Theoretic Analysis of Out-of-Distribution Generalization in Meta-Learning with Applications to Meta-RL
Control Synthesis with Reinforcement Learning: A Modeling Perspective
Dense and Diverse Goal Coverage in Multi Goal Reinforcement Learning
Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle