Wed, Feb 19, 7:00 PM GMT. 07 Apr 2020 in DeepLearning on ReinforcementLearning. Atari game은 RL community에서 오랫동안 벤치마크가 되어왔습니다. Online event. On-the-Fly Adaptation of Source Code Models using Meta-Learning NLP ReinforcementLearning #1626 opened Mar 31, 2020 by icoxfog417.

334 attendees; Deep Learning With Sets & Minimal Newton Solvers For Deep Learning. Martin G. Abdellatif B. ABIN A. Agent57: Outperforming the Atari Human Benchmark Optimization ReinforcementLearning #1628 opened Apr 1, 2020 by icoxfog417. The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide ... 続きを表示 The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide range of tasks. Also a comparison to human record: Montezuma's Revenge: "average human" score is 4753.30, Agent57 score is 9352.01, the human record is 1219200.0 Note2: top hacker news comments is interesting they compare it's complexity to expert systems, suggesting that it's been over-optimised to atari games. Agent57: Outperforming the Atari Human Benchmark.
… Agent57: Outperforming the Atari Human Benchmark written by Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell (Submitted on 30 Mar 2020) Comments: Published as a conference paper in ICLR 2020 Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Agent57: Outperforming the Human Atari Benchmark . Agent57: Outperforming the Atari Human Benchmarkを読む その4 - TadaoYamaokaの日記. DeepMind has announced that they have developed a Reinforcement Learning (RL) agent that outperforms human baselines in all 57 Atari games, as part of the Atari57 suite.. Back in 2012, the so-called Arcade Learning Environment or Atari57 benchmark was introduced as a collection of games that pose a number of challenges for reinforcement learning agents. (Image source: DeepMind Blog: “Agent57: Outperforming the human Atari benchmark”) Instead of using the Euclidean distance to measure closeness of states in episodic memory, Savinov, et al. See all. Deepmind's Agent57 AI learning agent learns to play 57 originally released games on the Atari 2600, outperforming average human players.
Overview of the DeepMind paper “Agent57: Outperforming the Atari Human Benchmark” by Badia et. AI learns to play original Atari 2600 games By Atari on 2020-05-24. See all. The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide range of tasks. Agent57: Outperforming the Atari Human Benchmark 논문 리뷰. Abstract.

1. Job detail page. Title:Agent57: Outperforming the Atari Human Benchmark. Agent57: Outperforming the human Atari benchmark | DeepMind. Authors:Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell Abstract: Atari games have been a long-standing benchmark in the reinforcement learning … al.Continue reading on deepgamingai » Source