Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?EI
Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and correspondingly diagnose individual actions against such a characterization. Here we consider a family of combinatorial games, arising from work of Erdos, Selfridge, and Spencer, and we propose their use as environments for evaluating and comparing different approaches to reinforcement learning. These games have a num...更多
- 1Michael L. Littman, Markov Games as a Framework for Multi-Agent Reinforcement Learning.ICML, pp. 157-163, 1994.
- 3Martin A. Zinkevich, Michael Bowling, Michael Wunder. The lemonade stand game competition: solving unsolvable games.SIGecom Exchanges, pp. 35-38, 2011.
- 6Aravind Rajeswaran, Sarvjeet Ghotra, Sergey Levine, Balaraman Ravindran. EPOpt: Learning Robust Neural Network Policies Using Model Ensembles.CoRR, 2016.
- 7Joshua Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, Pieter Abbeel. Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World.IROS, 2017.
- 8Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger. Deep Reinforcement Learning that Matters.AAAI, 2017.