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 10801–10825 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
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Benchmark Results

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