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 14111420 of 15113 papers

TitleStatusHype
Conservative Offline Distributional Reinforcement LearningCode1
Explore and Control with Adversarial SurpriseCode1
Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and ResultsCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal TransformersCode1
Offline Meta-Reinforcement Learning with Online Self-SupervisionCode1
Distributed Online Service Coordination Using Deep Reinforcement LearningCode1
THE SJTU SYSTEM FOR DCASE2021 CHALLENGE TASK 6: AUDIO CAPTIONING BASED ON ENCODER PRE-TRAINING AND REINFORCEMENT LEARNINGCode1
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement LearningCode1
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Benchmark Results

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