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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2151–2175 of 15113 papers

TitleStatusHype
Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulationsCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Deep Reinforcement Learning for Process SynthesisCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
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
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Compile Scene Graphs with Reinforcement LearningCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Compositional Reinforcement Learning from Logical SpecificationsCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Deep Symbolic Superoptimization Without Human KnowledgeCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Demonstration-Guided Reinforcement Learning with Learned SkillsCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
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Benchmark Results

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