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 2351–2375 of 15113 papers

TitleStatusHype
Learning from Trajectories via Subgoal DiscoveryCode0
Learning from Multiple Independent Advisors in Multi-agent Reinforcement LearningCode0
Learning from Sparse Offline Datasets via Conservative Density EstimationCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Learning Local Search Heuristics for Boolean SatisfiabilityCode0
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement LearningCode0
Learning from Demonstration without DemonstrationsCode0
Learning Dynamic Context Augmentation for Global Entity LinkingCode0
Learning-Driven Exploration for Reinforcement LearningCode0
BaRC: Backward Reachability Curriculum for Robotic Reinforcement LearningCode0
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 Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy GradientCode0
A Multilevel Reinforcement Learning Framework for PDE-based ControlCode0
Learning Curriculum Policies for Reinforcement LearningCode0
Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language ModelsCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Learning data augmentation policies using augmented random searchCode0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
Balancing the Scales: Reinforcement Learning for Fair ClassificationCode0
Learning by Playing - Solving Sparse Reward Tasks from ScratchCode0
Learning Complex Teamwork Tasks Using a Given Sub-task DecompositionCode0
Learning from Learners: Adapting Reinforcement Learning Agents to be Competitive in a Card GameCode0
Learning-based Model Predictive Control for Safe Exploration and Reinforcement LearningCode0
Balancing detectability and performance of attacks on the control channel of Markov Decision ProcessesCode0
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Benchmark Results

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