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 1101–1110 of 15113 papers

TitleStatusHype
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior RegularizationCode1
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and PlanningCode1
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement LearningCode1
Efficient Active Search for Combinatorial Optimization ProblemsCode1
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on GraphsCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
Show:102550
← PrevPage 111 of 1512Next →

Benchmark Results

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