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 1526–1550 of 15113 papers

TitleStatusHype
Learning Guidance Rewards with Trajectory-space SmoothingCode1
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
Learning Intrusion Prevention Policies through Optimal StoppingCode1
Learning Large Neighborhood Search Policy for Integer ProgrammingCode1
Learning Long-Term Reward Redistribution via Randomized Return DecompositionCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
Learning multiple gaits of quadruped robot using hierarchical reinforcement learningCode1
Learning of Parameters in Behavior Trees for Movement SkillsCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Learning Synthetic Environments for Reinforcement Learning with Evolution StrategiesCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Learning the Next Best View for 3D Point Clouds via Topological FeaturesCode1
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
Learning to Brachiate via Simplified Model ImitationCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience ReplayCode1
Learning to Map Natural Language Instructions to Physical Quadcopter Control using Simulated FlightCode1
Learning to Modulate pre-trained Models in RLCode1
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Learning to Optimize for Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
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Benchmark Results

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