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

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Showing 791–800 of 15113 papers

TitleStatusHype
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Can Wikipedia Help Offline Reinforcement Learning?Code1
Active Inference for Stochastic ControlCode1
CARL: Controllable Agent with Reinforcement Learning for Quadruped LocomotionCode1
Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority InfluenceCode1
Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous InferenceCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
Developmental Reinforcement Learning of Control Policy of a Quadcopter UAV with Thrust Vectoring RotorsCode1
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Benchmark Results

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