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Today's Reinforcement Learning Research Top Papers
Wednesday, November 5, 2025
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Introduces GasRL, a simulator coupling a calibrated natural gas market with deep reinforcement learning-trained storage policies. Achieves superior performance with SAC, optimizing stockpile management to affect equilibrium prices and market dynamics.
Proposes a reinforcement learning-based approach for data assimilation with unknown system dynamics. Leverages RL to construct a surrogate state transition model, overcoming reliance on pre-computed, noise-free training datasets.
Presents a novel traffic signal control framework combining Graph Attention Networks with Soft Actor-Critic RL. Models dynamic graph-structured traffic flow to optimize coordination between human-driven and autonomous vehicles.
Instantiates the SCoBots framework for interpretable neurosymbolic RL agents. Decomposes RL tasks into interpretable representations, addressing deep RL's shortcut learning and generalization issues from raw pixel states.
Proposes a path-coordinated continual learning framework combining Neural Tangent Kernel theory, statistical validation, and multiple path quality metrics. Addresses catastrophic forgetting by justifying plasticity bounds for improved performance.
Introduces SAIL-RL, a reinforcement learning post-training framework to enhance multimodal LLM reasoning. Teaches models when and how to think using dual-reward RL tuning, addressing outcome-only supervision and uniform thinking strategies.
Presents Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning for multi-task cooperative objectives. Uses automata to decompose tasks for agents, improving sample efficiency and enabling multi-task learning.
Develops a large-scale automatic treatment planning system for carbon ion therapy using parallel multi-agent reinforcement learning. Optimizes numerous treatment planning parameters to improve dose conformity and OAR sparing.
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