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 9026–9050 of 15113 papers

TitleStatusHype
Application of deep reinforcement learning for Indian stock trading automation—0
Learning and Information in Stochastic Networks and Queues—0
Gym-ANM: Open-source software to leverage reinforcement learning for power system management in research and education—0
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach—0
Behavior-based Neuroevolutionary Training in Reinforcement LearningCode0
Generic Itemset Mining Based on Reinforcement LearningCode0
Mean Field Games Flock! The Reinforcement Learning Way—0
RL-GRIT: Reinforcement Learning for Grammar Inference—0
RAIDER: Reinforcement-aided Spear Phishing Detector—0
Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting—0
Model-Based Offline Planning with Trajectory PruningCode0
DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling—0
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
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
Efficient PAC Reinforcement Learning in Regular Decision Processes—0
Feature-Based Interpretable Reinforcement Learning based on State-Transition Models—0
Adaptive Warm-Start MCTS in AlphaZero-like Deep Reinforcement Learning—0
Intelligence and Unambitiousness Using Algorithmic Information Theory—0
Online Algorithms and Policies Using Adaptive and Machine Learning Approaches—0
SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning—0
Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving—0
Principled Exploration via Optimistic Bootstrapping and Backward InductionCode0
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective—0
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Benchmark Results

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