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 1851–1900 of 15113 papers

TitleStatusHype
A Max-Min Entropy Framework for Reinforcement LearningCode1
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision TransformersCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Testing Stationarity and Change Point Detection in Reinforcement LearningCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Reinforcement Learning-powered Semantic Communication via Semantic SimilarityCode1
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Reinforcement Learning with Action-Free Pre-Training from VideosCode1
Reinforcement Learning with Augmented DataCode1
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Reinforcement Learning with Convex ConstraintsCode1
Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock PoolsCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
Reinforcement Learning with Random DelaysCode1
Reinforcement learning with a network of spiking agentsCode1
Reinforcement Learning with Token-level Feedback for Controllable Text GenerationCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
ReLIC: A Recipe for 64k Steps of In-Context Reinforcement Learning for Embodied AICode1
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image EnhancementCode1
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
ResAct: Reinforcing Long-term Engagement in Sequential Recommendation with Residual ActorCode1
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for RoboticsCode1
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
A Minimalist Approach to Offline Reinforcement LearningCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
Retrosynthetic Planning with Dual Value NetworksCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning ResearchCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Reward Constrained Policy OptimizationCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Reward Reports for Reinforcement LearningCode1
Reward Uncertainty for Exploration in Preference-based Reinforcement LearningCode1
Rewriting History with Inverse RL: Hindsight Inference for Policy ImprovementCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
AdaRefiner: Refining Decisions of Language Models with Adaptive FeedbackCode1
RLBenchNet: The Right Network for the Right Reinforcement Learning TaskCode1
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment TreesCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
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Benchmark Results

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