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 871–880 of 15113 papers

TitleStatusHype
Active Inference for Stochastic ControlCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Discriminator-Weighted Offline Imitation Learning from Suboptimal DemonstrationsCode1
Augmenting Policy Learning with Routines Discovered from a Single DemonstrationCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline GenerationCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
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Benchmark Results

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