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

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Showing 8426–8450 of 15113 papers

TitleStatusHype
Online Reinforcement Learning for Periodic MDP—0
Online Reinforcement Learning for Real-Time Exploration in Continuous State and Action Markov Decision Processes—0
Online Reinforcement Learning in Periodic MDP—0
Online Reinforcement Learning in Stochastic Games—0
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes—0
On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Online RL in Linearly q^π-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore—0
Online Robustness Training for Deep Reinforcement Learning—0
Online Robust Policy Learning in the Presence of Unknown Adversaries—0
Online Robust Reinforcement Learning with Model Uncertainty—0
Online Safety Assurance for Deep Reinforcement Learning—0
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning—0
Online Shielding for Reinforcement Learning—0
Online Sparse Reinforcement Learning—0
Asymptotic Analysis of Sample-averaged Q-learning—0
Online Sub-Sampling for Reinforcement Learning with General Function Approximation—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Online Transfer Learning in Reinforcement Learning Domains—0
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning—0
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning—0
On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning—0
On Lower Bounds for Regret in Reinforcement Learning—0
On mechanisms for transfer using landmark value functions in multi-task lifelong reinforcement learning—0
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Benchmark Results

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