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 10451–10475 of 15113 papers

TitleStatusHype
SAMBA: Safe Model-Based & Active Reinforcement LearningCode1
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Continuous Control for Searching and Planning with a Learned Model—0
Deep Reinforcement Learning for Neural Control—0
A Brief Look at Generalization in Visual Meta-Reinforcement Learning—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Decorrelated Double Q-learning—0
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Potential Field Guided Actor-Critic Reinforcement Learning—0
Mutual Information Based Knowledge Transfer Under State-Action Dimension MismatchCode0
TorsionNet: A Reinforcement Learning Approach to Sequential Conformer SearchCode1
Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling—0
Recurrent Sum-Product-Max Networks for Decision Making in Perfectly-Observed EnvironmentsCode0
Using Reinforcement Learning to Allocate and Manage Service Function Chains in Cellular Networks—0
Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue SystemCode1
Surveys without Questions: A Reinforcement Learning Approach—0
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation—0
Multi-Agent Informational Learning Processes—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Deep Reinforcement Learning for Electric Transmission Voltage Control—0
Closed Loop Neural-Symbolic Learning via Integrating Neural Perception, Grammar Parsing, and Symbolic ReasoningCode1
Multi-Agent Reinforcement Learning in Stochastic Networked SystemsCode0
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward—0
Zeroth-Order Supervised Policy Improvement—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
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Benchmark Results

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