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 1191–1200 of 15113 papers

TitleStatusHype
Contextualize Me -- The Case for Context in Reinforcement LearningCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Geometric Multimodal Contrastive Representation LearningCode1
Learning Synthetic Environments and Reward Networks for Reinforcement LearningCode1
Leveraging Approximate Symbolic Models for Reinforcement Learning via Skill DiversityCode1
Transfer Reinforcement Learning for Differing Action Spaces via Q-Network RepresentationsCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy MatchingCode1
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Benchmark Results

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