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 1901–1925 of 15113 papers

TitleStatusHype
"Good Robot!": Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real TransferCode1
Improving the Validity of Automatically Generated Feedback via Reinforcement LearningCode1
In-Context Decision Transformer: Reinforcement Learning via Hierarchical Chain-of-ThoughtCode1
Safe Reinforcement Learning via Curriculum InductionCode1
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning AgentsCode1
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing ConstraintCode1
Improving Generalization in Reinforcement Learning with Mixture RegularizationCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Gradient Surgery for Multi-Task LearningCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Improving Data Efficiency for LLM Reinforcement Fine-tuning Through Difficulty-targeted Online Data Selection and Rollout ReplayCode1
Graph Constrained Reinforcement Learning for Natural Language Action SpacesCode1
BabyAI 1.1Code1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic EnvironmentsCode1
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Benchmarking Reinforcement Learning Techniques for Autonomous NavigationCode1
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for RoboticsCode1
Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics MixtureCode1
Grid-to-Graph: Flexible Spatial Relational Inductive Biases for Reinforcement LearningCode1
Grounding Language to Entities and Dynamics for Generalization in Reinforcement LearningCode1
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and ExploitationCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
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Benchmark Results

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