This paper introduces mean field reinforcement learning through Markov decision processes in large-population stochastic control, developing the necessary framework for representative-agent learning,…

The paper introduces the Markov decision contest, a new framework for reinforcement learning using pairwise preferences, and proves that stationary Markov policies are optimal and solvable efficiently…

This paper provides a mathematical framework for studying different policy learning problems and shows reductions between them.

The paper introduces ReMax, a novel objective function that naturally encourages stochastic exploration in policy gradient reinforcement learning by evaluating expected maximum returns over multiple s…

This paper provides non-asytotic sample complexity guarantees for the Navigate and Stop algorithm in online tabular Reinforcement Learning, identifying additional attributes that affect the overall sa…

This paper theoretically justifies the strong performance of linear recurrent neural networks as memory units in partially observable reinforcement learning by constructing specific linear filters tha…

The paper proposes a Hierarchical Reinforcement Learning framework with two levels for handling high-level strategic planning and low-level continuous-control using Soft Actor-Critic and entropy-regul…

The paper introduces Prompted Policy Optimization (PromptPO), an LLM-based method that successfully optimizes policies for various sequential RL tasks, demonstrating that LLMs can replace classical RL…

The paper introduces the Terminal Representation (TR), a novel, lower-dimensional, and structurally distinct formulation for encoding reward-weighted trajectories in RL that bypasses the need for eige…

This paper develops a policy-learning framework to optimally assign prediction tasks to multiple agents, considering individual agent expertise and capacity constraints, achieving systematic performan…

This paper explores the application of explainability techniques to Reinforcement Learning algorithms in Air Traffic Control using a simplified environment and a saliency map.

This paper develops a new algorithm for policy optimization in online episodic tabular Markov decision processes with unknown transition kernels, providing data-dependent regret bounds and best-of-bot…

The paper introduces AgenticRL, a self-refining reinforcement learning framework that uses a multimodal GPT agent to automatically design, refine, and deploy reward functions for complex UAV navigatio…

The paper proposes DIBS, a decoupled behavioral cloning approach that stabilizes inductive generalization in RL by separating task-specific policy learning from the evolution function, leading to impr…

The paper introduces a novel shielding framework for Robust MDPs (RMDPs) that guarantees safety under worst-case transition probabilities, enabling safe reinforcement learning even when transition dyn…

This paper synthesizes expert knowledge from a workshop to provide a comprehensive framework and best-practice guidelines for developing high-quality reinforcement learning environments for autonomous…

A model-driven approach is proposed for generating families of reinforcement learning training environments using a hybrid genetic algorithm and model transformations.