271 citations · 524 across the 6 of their papers we have counts for
6 papers
On Inductive Biases in Deep Reinforcement Learning
Matteo Hessel, Hado van Hasselt, Joseph Modayil +1
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent's objective and its interface to the environment. These inductive biases can take many fo…
Reinforcement Learning with Unsupervised Auxiliary Tasks
Max Jaderberg, Volodymyr Mnih, Wojciech Marian Czarnecki +4
Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible t…
Learning and Transfer of Modulated Locomotor Controllers
Nicolas Heess, Greg Wayne, Yuval Tassa +3
We study a novel architecture and training procedure for locomotion tasks. A high-frequency, low-level "spinal" network with access to proprioceptive sensors learns sensorimotor pr…
Value Iteration with Options and State Aggregation
Kamil Ciosek, David Silver
This paper presents a way of solving Markov Decision Processes that combines state abstraction and temporal abstraction. Specifically, we combine state aggregation with the options…
Move Evaluation in Go Using Deep Convolutional Neural Networks
Chris J. Maddison, Aja Huang, Ilya Sutskever +1
The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep c…
Compositional Planning Using Optimal Option Models
David Silver, Kamil Ciosek
In this paper we introduce a framework for option model composition. Option models are temporal abstractions that, like macro-operators in classical planning, jump directly from a…