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 1501–1525 of 15113 papers

TitleStatusHype
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy OptimizationCode1
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement LearningCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
Uncertainty Weighted Actor-Critic for Offline Reinforcement LearningCode1
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with PybulletCode1
Spectral Normalisation for Deep Reinforcement Learning: an Optimisation PerspectiveCode1
A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless SystemsCode1
Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge GraphsCode1
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
Deep reinforcement learning-designed radiofrequency waveform in MRICode1
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise RolloutsCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
Deep Reinforcement Learning for Adaptive Exploration of Unknown EnvironmentsCode1
RL-IoT: Reinforcement Learning to Interact with IoT DevicesCode1
Constructions in combinatorics via neural networksCode1
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control ProblemCode1
Visual Navigation with Spatial AttentionCode1
Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement LearningCode1
Language Models are Few-Shot ButlersCode1
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Benchmark Results

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