26 citations · 51 across the 3 of their papers we have counts for
3 papers
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Gabriel V. de la Cruz, Yunshu Du, Matthew E. Taylor
Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa. Integrating prior…
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…
Pre-training Neural Networks with Human Demonstrations 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 a deep neural network as its function approximator and by learning dire…