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 961970 of 15113 papers

TitleStatusHype
Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweeningCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement LearningCode1
BAFFLE: Hiding Backdoors in Offline Reinforcement Learning DatasetsCode1
Winner Takes It All: Training Performant RL Populations for Combinatorial OptimizationCode1
Exploration via Planning for Information about the Optimal TrajectoryCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill DiscoveryCode1
Rainier: Reinforced Knowledge Introspector for Commonsense Question AnsweringCode1
Real-Time Reinforcement Learning for Vision-Based Robotics Utilizing Local and Remote ComputersCode1
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Benchmark Results

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