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 10501–10525 of 15113 papers

TitleStatusHype
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
On Memory Mechanism in Multi-Agent Reinforcement Learning—0
On Modeling Long-Term User Engagement from Stochastic Feedback—0
On Multi-Agent Learning in Team Sports Games—0
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning: Case Studies in Offline RL and Finetuning—0
On Neural Consolidation for Transfer in Reinforcement Learning—0
Towards Tractable Optimism in Model-Based Reinforcement Learning—0
On Optimistic versus Randomized Exploration in Reinforcement Learning—0
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization—0
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search—0
Subjective Reinforcement Learning for Open Complex Environments—0
Sublinear Least-Squares Value Iteration via Locality Sensitive Hashing—0
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Benchmark Results

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