暂无分享,去创建一个
Adam Lerer | Noam Brown | Hengyuan Hu | Jakob Foerster | Adam Lerer | Noam Brown | Hengyuan Hu | J. Foerster
[1] Joel Veness,et al. Monte-Carlo Planning in Large POMDPs , 2010, NIPS.
[2] Demis Hassabis,et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.
[3] H. Francis Song,et al. Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning , 2018, ICML.
[4] Jakob N. Foerster,et al. "Other-Play" for Zero-Shot Coordination , 2020, ICML.
[5] Tom Schaul,et al. Dueling Network Architectures for Deep Reinforcement Learning , 2015, ICML.
[6] Murray Campbell,et al. Deep Blue , 2002, Artif. Intell..
[7] Jakob N. Foerster,et al. Improving Policies via Search in Cooperative Partially Observable Games , 2019, AAAI.
[8] Julian Togelius,et al. Diverse Agents for Ad-Hoc Cooperation in Hanabi , 2019, 2019 IEEE Conference on Games (CoG).
[9] Demis Hassabis,et al. Mastering Atari, Go, chess and shogi by planning with a learned model , 2019, Nature.
[10] Brandon Cui,et al. Off-Belief Learning , 2021, ICML.
[11] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[12] Peter Stone,et al. Deep Recurrent Q-Learning for Partially Observable MDPs , 2015, AAAI Fall Symposia.
[13] Michael H. Bowling,et al. Rethinking Formal Models of Partially Observable Multiagent Decision Making , 2019, Artif. Intell..
[14] Noam Brown,et al. Superhuman AI for multiplayer poker , 2019, Science.
[15] Dimitri P. Bertsekas,et al. Rollout Algorithms for Stochastic Scheduling Problems , 1999, J. Heuristics.
[16] Noam Brown,et al. Superhuman AI for heads-up no-limit poker: Libratus beats top professionals , 2018, Science.
[17] Branislav Bosanský,et al. Solving Partially Observable Stochastic Games with Public Observations , 2019, AAAI.
[18] Gerald Tesauro,et al. TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play , 1994, Neural Computation.
[19] H. Francis Song,et al. The Hanabi Challenge: A New Frontier for AI Research , 2019, Artif. Intell..
[20] Joelle Pineau,et al. Online Planning Algorithms for POMDPs , 2008, J. Artif. Intell. Res..
[21] Lasse Becker-Czarnetzki. Report on DeepStack Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker , 2019 .
[22] Hengyuan Hu,et al. Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning , 2020, ICLR.
[23] Geoffrey J. Gordon,et al. Finding Approximate POMDP solutions Through Belief Compression , 2011, J. Artif. Intell. Res..
[25] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[26] Simon M. Lucas,et al. Evaluating and modelling Hanabi-playing agents , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).
[27] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[28] Kevin Waugh,et al. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.