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 2326–2350 of 15113 papers

TitleStatusHype
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
An agentic system with reinforcement-learned subsystem improvements for parsing form-like documentsCode0
Learning How to Active Learn by DreamingCode0
Learning Graph-Enhanced Commander-Executor for Multi-Agent NavigationCode0
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement LearningCode0
Learning Goal-Oriented Visual Dialog via Tempered Policy GradientCode0
Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement LearningCode0
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral SimilaritiesCode0
Batch Value-function Approximation with Only RealizabilityCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Learning Heuristics over Large Graphs via Deep Reinforcement LearningCode0
Learning to Compose Neural Networks for Question AnsweringCode0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Learning from Demonstration without DemonstrationsCode0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Combining imitation and deep reinforcement learning to accomplish human-level performance on a virtual foraging taskCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Learning Dynamic Context Augmentation for Global Entity LinkingCode0
Learning data augmentation policies using augmented random searchCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
An Actor-Critic Algorithm for Sequence PredictionCode0
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Benchmark Results

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