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 2421–2430 of 15113 papers

TitleStatusHype
Intelligent Agricultural Management Considering N_2O Emission and Climate Variability with Uncertainties—0
PRDP: Proximal Reward Difference Prediction for Large-Scale Reward Finetuning of Diffusion Models—0
Optimal Task Assignment and Path Planning using Conflict-Based Search with Precedence and Temporal Constraints—0
Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea—0
Hybrid Inverse Reinforcement LearningCode1
Auxiliary Reward Generation with Transition Distance Representation Learning—0
IR-Aware ECO Timing Optimization Using Reinforcement Learning—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
Natural Language Reinforcement Learning—0
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Benchmark Results

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