1.1k citations · 3.7k across the 8 of their papers we have counts for
9 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…
Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, Thomas Hubert, Julian Schrittwieser +10
The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques,…
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning
Marc Lanctot, Vinicius Zambaldi, Audrunas Gruslys +5
To achieve general intelligence, agents must learn how to interact with others in a shared environment: this is the challenge of multiagent reinforcement learning (MARL). The simpl…
Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt +7
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can…
StarCraft II: A New Challenge for Reinforcement Learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov +22
This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for re…
Emergence of Locomotion Behaviours in Rich Environments
Nicolas Heess, Dhruva TB, Srinivasan Sriram +9
The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals. In practice, however, it is common to carefully…