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 1151–1200 of 15113 papers

TitleStatusHype
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Contrastive Reinforcement Learning of Symbolic Reasoning DomainsCode1
SUBER: An RL Environment with Simulated Human Behavior for Recommender SystemsCode1
Contrastive State Augmentations for Reinforcement Learning-Based Recommender SystemsCode1
Controlgym: Large-Scale Control Environments for Benchmarking Reinforcement Learning AlgorithmsCode1
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Control-Oriented Model-Based Reinforcement Learning with Implicit DifferentiationCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
A Deep Reinforcement Learning Framework for the Financial Portfolio Management ProblemCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement LearningCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Cross-Embodiment Robot Manipulation Skill Transfer using Latent Space AlignmentCode1
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with PybulletCode1
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and SimplicityCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning ApproachCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
An Inductive Bias for Distances: Neural Nets that Respect the Triangle InequalityCode1
Curriculum-based Asymmetric Multi-task Reinforcement LearningCode1
Curriculum Offline Imitation LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Data-Efficient Reinforcement Learning with Self-Predictive RepresentationsCode1
DataLight: Offline Data-Driven Traffic Signal ControlCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value FunctionCode1
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod RobotCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Deceptive Path Planning via Reinforcement Learning with Graph Neural NetworksCode1
Concise Reasoning via Reinforcement LearningCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Deep Active Inference for Partially Observable MDPsCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Deep Implicit Coordination Graphs for Multi-agent Reinforcement LearningCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
Approximate information state for approximate planning and reinforcement learning in partially observed systemsCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
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Benchmark Results

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