On Reinforcement Learning for Full-length Game of StarCraft

Introduction to reinforcement learning

R. Sutton and A. Barto · 1998

Earlier work this paper cites.

Hierarchical reinforcement learning with the MAXQ value function decomposition

T. G. Dietterich · 1999

Earlier work this paper cites.

Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning

R. S. Sutton, D. Precup, and S. P. Singh · 1999

Earlier work this paper cites.

State abstraction for programmable reinforcement learning agents

D. Andre and S. J. Russell · 2002

Earlier work this paper cites.

Clospan: Mining closed sequential patterns in large datasets

X. Yan, J. Han, and R. Afshar · 2003

Earlier work this paper cites.

Playing atari with deep reinforcement learning

Original

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller · 2013

Earlier work this paper cites.

A survey of real-time strategy game AI research and competition in starcraft

S. Ontañón, G. Synnaeve, A. Uriarte, F. Richoux, D. Churchill, and M. Preuss · 2013

Earlier work this paper cites.

End-to-end training of deep visuomotor policies

S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016

Earlier work this paper cites.

Asynchronous methods for deep reinforcement learning

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016

Cited alongside, same era.

Safe, multi-agent, reinforcement learning for autonomous driving

Original

S. Shalev-Shwartz, S. Shammah, and A. Shashua · 2016

Cited alongside, same era.

Mastering the game of go with deep neural networks and tree search

D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. P. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis · 2016

Cited alongside, same era.

Episodic exploration for deep deterministic policies: An application to starcraft micromanagement tasks

Original

N. Usunier, G. Synnaeve, Z. Lin, and S. Chintala · 2016

Cited alongside, same era.

The option-critic architecture

P. Bacon, J. Harb, and D. Precup · 2017

Proximal policy optimization algorithms

Original

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017

Later among the works it cites.

Mastering the game of go without human knowledge

D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al · 2017

Later among the works it cites.

ELF: an extensive, lightweight and flexible research platform for real-time strategy games

Y. Tian, Q. Gong, W. Shang, Y. Wu, and C. L. Zitnick · 2017

Later among the works it cites.

Feudal networks for hierarchical reinforcement learning

A. S. Vezhnevets, S. Osindero, T. Schaul, N. Heess, M. Jaderberg, D. Silver, and K. Kavukcuoglu · 2017

Later among the works it cites.

Starcraft II: A new challenge for reinforcement learning

Original

O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Küttler, J. Agapiou, J. Schrittwieser, J. Quan, S. Gaffney, S. Petersen, K. Simonyan, T. Schaul, H. van Hasselt, D. Silver, T. P. Lillicrap, K. Calderone, P. Keet, A. Brunasso, D. Lawrence, A. Ekermo, J. Repp, and R. Tsing · 2017