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 2501–2550 of 15113 papers

TitleStatusHype
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Avoiding Jammers: A Reinforcement Learning Approach—0
Avoiding Catastrophic States with Intrinsic Fear—0
Adaptive Reinforcement Learning for Unobservable Random Delays—0
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management—0
Control of Renewable Energy Communities using AI and Real-World Data—0
Control of synaptic plasticity via the fusion of reinforcement learning and unsupervised learning in neural networks—0
Avoidance Learning Using Observational Reinforcement Learning—0
A Visual Communication Map for Multi-Agent Deep Reinforcement Learning—0
A Model-Based Reinforcement Learning Approach for PID Design—0
A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidance—0
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task—0
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos—0
Average-Reward Reinforcement Learning with Entropy Regularization—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Average-Reward Reinforcement Learning with Trust Region Methods—0
Average Reward Reinforcement Learning with Monotonic Policy Improvement—0
A model-based approach to meta-Reinforcement Learning: Transformers and tree search—0
Adaptive Q-learning for Interaction-Limited Reinforcement Learning—0
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites—0
Unknown mixing times in apprenticeship and reinforcement learning—0
Average Reward Reinforcement Learning for Omega-Regular and Mean-Payoff Objectives—0
A Model-based Approach for Sample-efficient Multi-task Reinforcement Learning—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
Average-reward model-free reinforcement learning: a systematic review and literature mapping—0
AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Average-Reward Maximum Entropy Reinforcement Learning for Underactuated Double Pendulum Tasks—0
Average-Reward Learning and Planning with Options—0
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications—0
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning—0
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference—0
A Benchmark for Low-Switching-Cost Reinforcement Learning—0
Control of Memory, Active Perception, and Action in Minecraft—0
Average Cost Optimal Control of Stochastic Systems Using Reinforcement Learning—0
ACPO: A Policy Optimization Algorithm for Average MDPs with Constraints—0
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
A Validation Tool for Designing Reinforcement Learning Environments—0
Adaptive Policy Transfer in Reinforcement Learning—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
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Benchmark Results

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