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 1301–1310 of 15113 papers

TitleStatusHype
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning ApproachCode1
Trust Region-Based Safe Distributional Reinforcement Learning for Multiple ConstraintsCode1
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement LearningCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
A Crash Course on Reinforcement LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Show:102550
← PrevPage 131 of 1512Next →

Benchmark Results

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