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 5351–5400 of 15113 papers

TitleStatusHype
The Integration of Machine Learning into Automated Test Generation: A Systematic Mapping Study—0
Robust Deep Reinforcement Learning through Bootstrapped Opportunistic CurriculumCode1
Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization—0
Finding Optimal Policy for Queueing Models: New Parameterization—0
A Single-Timescale Analysis For Stochastic Approximation With Multiple Coupled Sequences—0
Federated Stochastic Approximation under Markov Noise and Heterogeneity: Applications in Reinforcement Learning—0
Incorporating Voice Instructions in Model-Based Reinforcement Learning for Self-Driving Cars—0
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution EngineCode5
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy—0
Deep reinforced active learning for multi-class image classification—0
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay BufferCode1
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?—0
Benchmarking Constraint Inference in Inverse Reinforcement LearningCode1
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming—0
From Multi-agent to Multi-robot: A Scalable Training and Evaluation Platform for Multi-robot Reinforcement Learning—0
EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RLCode1
Guided Safe Shooting: model based reinforcement learning with safety constraints—0
DNA: Proximal Policy Optimization with a Dual Network ArchitectureCode1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic ExplorationCode1
Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG—0
A Survey on Model-based Reinforcement Learning—0
Guarantees for Epsilon-Greedy Reinforcement Learning with Function Approximation—0
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology—0
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder—0
The State of Sparse Training in Deep Reinforcement LearningCode0
SMPL: Simulated Industrial Manufacturing and Process Control Learning EnvironmentsCode1
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving—0
Bootstrapped Transformer for Offline Reinforcement Learning—0
Fast Population-Based Reinforcement Learning on a Single MachineCode1
Generalised Policy Improvement with Geometric Policy Composition—0
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Reinforcement Learning for Economic Policy: A New Frontier?—0
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based ImaginationCode0
Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming—0
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers—0
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning—0
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective—0
Training Discrete Deep Generative Models via Gapped Straight-Through EstimatorCode1
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Towards a Solution to Bongard Problems: A Causal Approach—0
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement LearningCode0
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Benchmark Results

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