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

Showing 16011610 of 15113 papers

TitleStatusHype
Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement LearningCode1
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
Scalable Bayesian Inverse Reinforcement LearningCode1
Multi-Task Reinforcement Learning with Context-based RepresentationsCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Risk-Averse Offline Reinforcement LearningCode1
Domain Adaptation In Reinforcement Learning Via Latent Unified State RepresentationCode1
Reverb: A Framework For Experience ReplayCode1
Continuous-Time Model-Based Reinforcement LearningCode1
rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching TasksCode1
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Benchmark Results

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