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 241–250 of 15113 papers

TitleStatusHype
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
Aligning AI With Shared Human ValuesCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Benchmarking Potential Based Rewards for Learning Humanoid LocomotionCode2
Efficient Online Reinforcement Learning with Offline DataCode2
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Benchmark Results

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