SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 1421–1430 of 15113 papers

TitleStatusHype
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Streaming Deep Reinforcement Learning Finally WorksCode3
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement LearningCode1
Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach—0
MarineFormer: A Spatio-Temporal Attention Model for USV Navigation in Dynamic Marine Environments—0
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
Guided Reinforcement Learning for Robust Multi-Contact Loco-Manipulation—0
Integrating Large Language Models and Reinforcement Learning for Non-Linear Reasoning—0
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified