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 12811290 of 15113 papers

TitleStatusHype
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
DROPO: Sim-to-Real Transfer with Offline Domain RandomizationCode1
Active Exploration for Inverse Reinforcement LearningCode1
DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment RegimeCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom RepresentationsCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
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Benchmark Results

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