4 papers
Novelty Search in Representational Space for Sample Efficient Exploration
Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau
We present a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objective…
An Introduction to Deep Reinforcement Learning
Vincent Francois-Lavet, Peter Henderson, Riashat Islam +2
Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-maki…
Combined Reinforcement Learning via Abstract Representations
Vincent François-Lavet, Yoshua Bengio, Doina Precup +1
In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly…
Reward Estimation for Variance Reduction in Deep Reinforcement Learning
Joshua Romoff, Peter Henderson, Alexandre Piché +2
Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high varia…