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 2291–2300 of 15113 papers

TitleStatusHype
Decomposing Control Lyapunov Functions for Efficient Reinforcement LearningCode0
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight—0
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents—0
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data—0
Offline Multitask Representation Learning for Reinforcement Learning—0
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards—0
The Value of Reward Lookahead in Reinforcement Learning—0
Reinforcement Learning with Token-level Feedback for Controllable Text GenerationCode1
Causality from Bottom to Top: A Survey—0
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation—0
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Benchmark Results

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