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 931940 of 15113 papers

TitleStatusHype
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
A Meta-Reinforcement Learning Algorithm for Causal DiscoveryCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Distilling Reinforcement Learning Tricks for Video GamesCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic ControlCode1
Accelerating Exploration with Unlabeled Prior DataCode1
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
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Benchmark Results

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