Search Results for author:
Found 9 papers, 5 papers with code
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery.
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning.
Investigation of Independent Reinforcement Learning Algorithms in Multi-Agent Environments
We show that in fully-observable environments, independent algorithms can perform on par with multi-agent algorithms in cooperative and competitive settings.
Multi-Agent Advisor Q-Learning
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible.
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning
We aim to bridge the gap between theoretical and empirical work in $Q$-function reuse by providing some theoretical insights on the effectiveness of $Q$-function reuse when applied to the $Q$-learning with UCB-Hoeffding algorithm.
Partially Observable Mean Field Reinforcement Learning
Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents.
Multi-agent Reinforcement Learning
Q-Learning
Multiagent Systems
Maximum Reward Formulation In Reinforcement Learning
Reinforcement learning (RL) algorithms typically deal with maximizing the expected cumulative return (discounted or undiscounted, finite or infinite horizon).
A review of machine learning applications in wildfire science and management
Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems.
Multi Type Mean Field Reinforcement Learning
We consider two different kinds of mean field environments: a) Games where agents belong to predefined types that are known a priori and b) Games where the type of each agent is unknown and therefore must be learned based on observations.
