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 401–425 of 15113 papers

TitleStatusHype
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric VehiclesCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Controlling the Risk of Conversational Search via Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
A Benchmark Environment Motivated by Industrial Control ProblemsCode1
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Benchmark Results

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