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 17011710 of 15113 papers

TitleStatusHype
Reinforcement Learning with Videos: Combining Offline Observations with InteractionCode1
pymgrid: An Open-Source Python Microgrid Simulator for Applied Artificial Intelligence ResearchCode1
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
Geometric Deep Reinforcement Learning for Dynamic DAG SchedulingCode1
f-IRL: Inverse Reinforcement Learning via State Marginal MatchingCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Drafting in Collectible Card Games via Reinforcement LearningCode1
A Reinforcement Learning Approach to the Orienteering Problem with Time WindowsCode1
RealAnt: An Open-Source Low-Cost Quadruped for Education and Research in Real-World Reinforcement LearningCode1
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Benchmark Results

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