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 551–600 of 15113 papers

TitleStatusHype
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
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
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
Critic-Guided Decoding for Controlled Text GenerationCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
Critic Regularized RegressionCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
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
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Co-Reinforcement Learning for Unified Multimodal Understanding and GenerationCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
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
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
A2C is a special case of PPOCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
Continuous MDP Homomorphisms and Homomorphic Policy GradientCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Continuous-Time Model-Based Reinforcement LearningCode1
Continuous control with deep reinforcement learningCode1
Continual World: A Robotic Benchmark For Continual Reinforcement LearningCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Contrastive Active InferenceCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Continual Model-Based Reinforcement Learning with HypernetworksCode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
6GAN: IPv6 Multi-Pattern Target Generation via Generative Adversarial Nets with Reinforcement LearningCode1
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable EnvironmentCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
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Benchmark Results

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