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

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Showing 2401–2425 of 15113 papers

TitleStatusHype
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
Backprop-Q: Generalized Backpropagation for Stochastic Computation GraphsCode0
Adaptive Reward Design for Reinforcement LearningCode0
A Monte Carlo AIXI ApproximationCode0
Learning Fair Policies in Multiobjective (Deep) Reinforcement Learning with Average and Discounted RewardsCode0
Backpropagation through the Void: Optimizing control variates for black-box gradient estimationCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Learning Dynamic Context Augmentation for Global Entity LinkingCode0
Backplay: "Man muss immer umkehren"Code0
Learning from Trajectories via Subgoal DiscoveryCode0
Learning Local Search Heuristics for Boolean SatisfiabilityCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
B2RL: An open-source Dataset for Building Batch Reinforcement LearningCode0
Learning by Playing - Solving Sparse Reward Tasks from ScratchCode0
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Learning Approximate Stochastic Transition ModelsCode0
Learning Bellman Complete Representations for Offline Policy EvaluationCode0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
A Model-Based Reinforcement Learning with Adversarial Training for Online RecommendationCode0
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural NetworksCode0
Model-Based Reinforcement Learning with Adversarial Training for Online RecommendationCode0
Learning and reusing primitive behaviours to improve Hindsight Experience Replay sample efficiencyCode0
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Benchmark Results

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