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 1891–1900 of 15113 papers

TitleStatusHype
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian ProcessesCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
DREAM: Deep Regret minimization with Advantage baselines and Model-free learningCode1
Learning Invariant Representations for Reinforcement Learning without ReconstructionCode1
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Automatic Curriculum Learning through Value DisagreementCode1
Forgetful Experience Replay in Hierarchical Reinforcement Learning from DemonstrationsCode1
Neural Ordinary Differential Equation Control of Dynamics on GraphsCode1
Learning to Track Dynamic Targets in Partially Known EnvironmentsCode1
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Benchmark Results

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