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 2151–2175 of 15113 papers

TitleStatusHype
Language Reward Modulation for Pretraining Reinforcement LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
CommonPower: A Framework for Safe Data-Driven Smart Grid ControlCode1
Communicative Reinforcement Learning Agents for Landmark Detection in Brain ImagesCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement LearningCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Compile Scene Graphs with Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
Know Your Action Set: Learning Action Relations for Reinforcement LearningCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Concise Reasoning via Reinforcement LearningCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control OptimizationCode1
KnowRL: Exploring Knowledgeable Reinforcement Learning for FactualityCode1
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Benchmark Results

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