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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1971–1980 of 15113 papers

TitleStatusHype
First return, then exploreCode1
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement LearningCode1
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
CFR-RL: Traffic Engineering with Reinforcement Learning in SDNCode1
Self-Paced Deep Reinforcement LearningCode1
Model-Based Meta-Reinforcement Learning for Flight with Suspended PayloadsCode1
Chip Placement with Deep Reinforcement LearningCode1
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty EstimationCode1
Energy-Based Imitation LearningCode1
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Benchmark Results

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