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 501–550 of 15113 papers

TitleStatusHype
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Cross-Modal Domain Adaptation for Reinforcement LearningCode1
A Composable Specification Language for Reinforcement Learning TasksCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Adaptive Transformers in RLCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Design Process is a Reinforcement Learning ProblemCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
Critic Regularized RegressionCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
Curriculum Offline Imitation LearningCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement LearningCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Learning to Manipulate Deformable Objects without DemonstrationsCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Adaptive Contention Window Design using Deep Q-learningCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Contrastive Active InferenceCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
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Benchmark Results

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