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 1501–1550 of 15113 papers

TitleStatusHype
KnowRL: Exploring Knowledgeable Reinforcement Learning for FactualityCode1
Know Your Action Set: Learning Action Relations for Reinforcement LearningCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Lane Change Decision-Making through Deep Reinforcement LearningCode1
Language-Conditioned Reinforcement Learning to Solve Misunderstandings with Action CorrectionsCode1
Language Instructed Reinforcement Learning for Human-AI CoordinationCode1
Language Reward Modulation for Pretraining Reinforcement LearningCode1
Large Batch Experience ReplayCode1
Large Language Model as a Policy Teacher for Training Reinforcement Learning AgentsCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
Learning a Decentralized Multi-arm Motion PlannerCode1
Learning agile and dynamic motor skills for legged robotsCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and DemonstrationsCode1
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
Learning Diverse Risk Preferences in Population-based Self-playCode1
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic EnvironmentsCode1
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement LearningCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
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