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 361–370 of 15113 papers

TitleStatusHype
Dialogue Learning With Human-In-The-LoopCode2
MT-R1-Zero: Advancing LLM-based Machine Translation via R1-Zero-like Reinforcement LearningCode2
Decoupling Representation Learning from Reinforcement LearningCode2
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Benchmarking Potential Based Rewards for Learning Humanoid LocomotionCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
DayDreamer: World Models for Physical Robot LearningCode2
Neuro-Nav: A Library for Neurally-Plausible Reinforcement LearningCode2
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
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Benchmark Results

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