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 10801–10850 of 15113 papers

TitleStatusHype
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response—0
Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections—0
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real—0
Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks—0
ShieldNN: A Provably Safe NN Filter for Unsafe NN Controllers—0
Parameter-Based Value FunctionsCode0
Preference-based Reinforcement Learning with Finite-Time Guarantees—0
Task-agnostic Exploration in Reinforcement Learning—0
Model Embedding Model-Based Reinforcement Learning—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
Reinforcement Learning Control of Robotic Knee with Human in the Loop by Flexible Policy Iteration—0
Solving the Order Batching and Sequencing Problem using Deep Reinforcement Learning—0
Online Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven—0
Index Selection for NoSQL Database with Deep Reinforcement Learning—0
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning—0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning—0
An online evolving framework for advancing reinforcement-learning based automated vehicle control—0
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Variable Gain Gradient Descent-based Reinforcement Learning for Robust Optimal Tracking Control of Uncertain Nonlinear System with Input-Constraints—0
Multiagent Reinforcement Learning based Energy Beamforming ControlCode0
Optimistic Distributionally Robust Policy OptimizationCode0
Non-local Policy Optimization via Diversity-regularized Collaborative Exploration—0
Reinforcement Learning with Supervision from Noisy Demonstrations—0
Tackling Morpion Solitaire with AlphaZero-likeRanked Reward Reinforcement Learning—0
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems—0
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning—0
Reinforcement Learning as Iterative and Amortised Inference—0
StarCraft II Build Order Optimization using Deep Reinforcement Learning and Monte-Carlo Tree Search—0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks—0
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning—0
Potential Field Guided Actor-Critic Reinforcement Learning—0
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed EnvironmentsCode0
Decorrelated Double Q-learning—0
Hierarchical reinforcement learning for efficent exploration and transfer—0
Exchangeable Models in Meta Reinforcement LearningCode0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning—0
Generalizing Curricula for Reinforcement Learning—0
Learning Intrinsically Motivated Options to Stimulate Policy Exploration—0
Deep Reinforcement Learning for Neural Control—0
A Brief Look at Generalization in Visual Meta-Reinforcement Learning—0
Logical Composition in Lifelong Reinforcement Learning—0
Bridging Worlds in Reinforcement Learning with Model-Advantage—0
Continuous Control for Searching and Planning with a Learned Model—0
Deep Reinforcement Learning for Electric Transmission Voltage Control—0
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
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Benchmark Results

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