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 12311240 of 15113 papers

TitleStatusHype
CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer LearningCode1
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement LearningCode1
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement LearningCode1
C-COMA: A CONTINUAL REINFORCEMENT LEARNING MODEL FOR DYNAMIC MULTIAGENT ENVIRONMENTSCode1
Visual Grounding for Object-Level Generalization in Reinforcement LearningCode1
CDT: Cascading Decision Trees for Explainable Reinforcement LearningCode1
Cell-Free Latent Go-ExploreCode1
Celebrating Diversity in Shared Multi-Agent Reinforcement LearningCode1
Certified Reinforcement Learning with Logic GuidanceCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
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Benchmark Results

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