45 citations · 145 across the 15 of their papers we have counts for
6 papers · 1 filter
Pre-training with Non-expert Human Demonstration for Deep Reinforcement Learning
Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Deep reinforcement learning (deep RL) has achieved superior performance in complex sequential tasks by using deep neural networks as function approximators to learn directly from r…
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power. However, there are still several challenges…
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach
Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. Decomposition of tasks into a hierarchi…
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have…
Interactive Reinforcement Learning with Dynamic Reuse of Prior Knowledge from Human/Agent's Demonstration
Zhaodong Wang, Matthew E. Taylor
Reinforcement learning has enjoyed multiple successes in recent years. However, these successes typically require very large amounts of data before an agent achieves acceptable per…
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control
Kenny Young, Baoxiang Wang, Matthew E. Taylor
Reinforcement learning (RL) has had many successes in both "deep" and "shallow" settings. In both cases, significant hyperparameter tuning is often required to achieve good perform…