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 501–525 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
Deep Reinforcement Learning For Sequence to Sequence ModelsCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly DataCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Deep Reinforcement Learning from Self-Play in Imperfect-Information GamesCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Adaptive Transformers in RLCode1
Deep Reinforcement Learning with Double Q-learningCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive Active InferenceCode1
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Benchmark Results

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