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 451–475 of 15113 papers

TitleStatusHype
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Contrastive Variational Reinforcement Learning for Complex ObservationsCode1
Critic-Guided Decision Transformer for Offline Reinforcement LearningCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
Cross-Modal Contrastive Learning of Representations for Navigation using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental ConditionsCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Curriculum Offline Imitation LearningCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
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Benchmark Results

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