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 19811990 of 15113 papers

TitleStatusHype
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Snooping Attacks on Deep Reinforcement LearningCode1
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement LearningCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Soft Actor-Critic Deep Reinforcement Learning for Fault Tolerant Flight ControlCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object ManipulationCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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Benchmark Results

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