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 15111520 of 15113 papers

TitleStatusHype
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Latent-Variable Advantage-Weighted Policy Optimization for Offline RLCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement LearningCode1
Learning agile and dynamic motor skills for legged robotsCode1
Learning Associative Inference Using Fast Weight MemoryCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

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