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

Showing 701725 of 15113 papers

TitleStatusHype
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
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
Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement LearningCode1
Affordance Learning from Play for Sample-Efficient Policy LearningCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement LearningCode1
Agent with Warm Start and Adaptive Dynamic Termination for Plane Localization in 3D UltrasoundCode1
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
Constrained Policy Optimization via Bayesian World ModelsCode1
Curious Hierarchical Actor-Critic Reinforcement LearningCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
CurricuLLM: Automatic Task Curricula Design for Learning Complex Robot Skills using Large Language ModelsCode1
Curriculum Offline Imitation LearningCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Constructions in combinatorics via neural networksCode1
Distributed Multi-Agent Reinforcement Learning with One-hop Neighbors and Compute Straggler MitigationCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLCode1
CropGym: a Reinforcement Learning Environment for Crop ManagementCode1
Debiased Contrastive LearningCode1
Decentralized Motion Planning for Multi-Robot Navigation using Deep Reinforcement LearningCode1
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement LearningCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
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Benchmark Results

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