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 17411750 of 15113 papers

TitleStatusHype
Evaluating Long-Term Memory in 3D MazesCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample ComplexityCode1
Evolutionary Planning in Latent SpaceCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
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Benchmark Results

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