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 1211–1220 of 15113 papers

TitleStatusHype
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human PlayerCode1
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
A Distributional Perspective on Reinforcement LearningCode1
Building a 3-Player Mahjong AI using Deep Reinforcement LearningCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
CaiRL: A High-Performance Reinforcement Learning Environment ToolkitCode1
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-TuningCode1
AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language ModelsCode1
Automating DBSCAN via Deep Reinforcement LearningCode1
Emergent collective intelligence from massive-agent cooperation and competitionCode1
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Benchmark Results

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