Distributed deep reinforcement learning: Learn how to play atari games in 21 minutes. CoRR, abs/1801.02852, 2018.Adamski, I., Adamski, R., Grel, T., Jdrych, A., Kaczmarek, K., Michalewski. H. (2018). Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 ...
A 2013 publication by DeepMind titled ‘Playing Atari with Deep Reinforcement Learning’ introduced a new deep learning model for reinforcement learning, and demonstrated its ability to master difficult control policies for Atari 2600 computer games, using only raw pixels as input. In this tutorial, ...
In 2013, London AI startup DeepMind created a great breakthrough in learning to control agents directly from high-dimensional sensory inputs. They published a paper, Playing Atari with Deep Reinforcement Learning, in which they showed how they taught an artificial neural network to play Atari game...
In: International Conference on Machine Learning, pp. 1928–1937. PMLR (2016) Google Scholar Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013) Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540...
The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. The Arcade Learning Environment (ALE) provides a set of Atari games that represent a useful benchma...
Goal: this paper aims to improve Deep Q Network and implement it for real-time Atari game playing. Tree-based method is used here. 1 Problem Problem 1) Reinforcement Learning (RL) have not directly addressed problems of perception, but has good theory and algorithms for policy selection. ...
Playing with bone and fat Playing Atari with Deep Reinforcement Learning Sperm in competition: not playing by the numbers Playing an Action Video Game Reduces Gender Differences in Spatial Cognition Playing for Real: Video Games and Stories for Health-Related Behavior Change ...
[9] Degrave, J. et al. Magnetic control of tokamak plasmas through deep reinforcement learning. Nature 602, 414-419 (2022). [10] Schrittwieser J, Antonoglou I, Hubert T, et al. Mastering atari, go, chess and shogi by planning with a learned model[J]. Nature, 2020, 588(7839): ...
Learning good feature representations is important for deep reinforcement learning (RL). However, with limited experience, RL often suffers from data inefficiency for training. For un-experienced or less-experienced trajectories (i.e., state-action sequences), the lack of data limits the use of ...
Recently, the deep reinforcement learning has shown successful outcomes in classic video games (e.g., ATARI) and visual doom competition. Although it's very powerful, it suffers from very long learning time to generalize its performance. For example, it takes about 7~15 days to produce a goo...