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 861–870 of 15113 papers

TitleStatusHype
Lexicographic Multi-Objective Reinforcement LearningCode1
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
Example-guided learning of stochastic human driving policies using deep reinforcement learningCode1
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence SummarizationCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
On Reinforcement Learning for the Game of 2048Code1
Offline Reinforcement Learning for Visual NavigationCode1
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
MoDem: Accelerating Visual Model-Based Reinforcement Learning with DemonstrationsCode1
Reinforcement Learning and Tree Search Methods for the Unit Commitment ProblemCode1
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Benchmark Results

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