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 351–360 of 15113 papers

TitleStatusHype
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
MBRL-Lib: A Modular Library for Model-based Reinforcement LearningCode2
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlCode2
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics ModelsCode2
DayDreamer: World Models for Physical Robot LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
Decoupling Representation Learning from Reinforcement LearningCode2
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Benchmark Results

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