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 8226–8250 of 15113 papers

TitleStatusHype
RAIDER: Reinforcement-aided Spear Phishing Detector—0
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting—0
Mean Field Games Flock! The Reinforcement Learning Way—0
Behavior-based Neuroevolutionary Training in Reinforcement LearningCode0
Generic Itemset Mining Based on Reinforcement LearningCode0
DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling—0
Model-Based Offline Planning with Trajectory PruningCode0
Regret Minimization Experience Replay in Off-Policy Reinforcement LearningCode0
Ordering-Based Causal Discovery with Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models—0
Efficient PAC Reinforcement Learning in Regular Decision Processes—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving—0
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches—0
Principled Exploration via Optimistic Bootstrapping and Backward InductionCode0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Intelligence and Unambitiousness Using Algorithmic Information Theory—0
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning—0
A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks—0
Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed TrafficCode1
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective—0
Acting upon Imagination: when to trust imagined trajectories in model based reinforcement learning—0
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with PybulletCode1
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control—0
Show:102550
← PrevPage 330 of 605Next →

Benchmark Results

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