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 2151–2200 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution SystemsCode1
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB trafficCode1
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Conservative Q-Learning for Offline Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Denoised MDPs: Learning World Models Better Than the World ItselfCode1
De novo PROTAC design using graph-based deep generative modelsCode1
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement LearningCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Confidence Estimation Transformer for Long-term Renewable Energy Forecasting in Reinforcement Learning-based Power Grid DispatchingCode1
Conditional Mutual Information for Disentangled Representations in Reinforcement LearningCode1
Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future DirectionsCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Blockchain Framework for Artificial Intelligence ComputationCode1
Compile Scene Graphs with Reinforcement LearningCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Concise Reasoning via Reinforcement LearningCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Bridging Imagination and Reality for Model-Based Deep Reinforcement LearningCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Discovering Minimal Reinforcement Learning EnvironmentsCode1
Discrete Codebook World Models for Continuous ControlCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
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Benchmark Results

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