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 9911000 of 15113 papers

TitleStatusHype
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter EfficientCode1
Integrating Saliency Ranking and Reinforcement Learning for Enhanced Object DetectionCode1
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement LearningCode1
Decomposed Soft Actor-Critic Method for Cooperative Multi-Agent Reinforcement LearningCode1
An Alternative Softmax Operator for Reinforcement LearningCode1
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
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Benchmark Results

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