Deep Q-Network (DQN) Learning to Play Atari Space Invaders | Deep Reinforcement Learning | PyTorch скачать в хорошем качестве

Deep Q-Network (DQN) Learning to Play Atari Space Invaders | Deep Reinforcement Learning | PyTorch

Deep Q-Network (DQN) Learning to Play Atari Space Invaders | Deep Reinforcement Learning | PyTorch Code: https://github.com/raphaelsenn/atari-dqn Original paper: https://www.nature.com/articles/natur... 00:00 Description 00:12 1 hour of training 00:59 2 hours of training 01:35 4 hours of training 02:37 8 hours of training 03:44 16 hours of training 05:02 32 hours of training Experimental setup: OS: Fedora Linux 42 (Workstation Edition) x86_64 CPU: AMD Ryzen 5 2600X (12) @ 3.60 GHz GPU: NVIDIA GeForce RTX 3060 ti (8GB VRAM) RAM: 32 GB DDR4 3200 MHz Training took ~32 hours on an RTX 3060ti (8GB VRAM). I trained DQN for 50 million environment steps using a replay buffer of 500,000 transitions (RAM limited :D), while the original DeepMind setup used 1,000,000. Citation: Mnih, V., Kavukcuoglu, K., Silver, D. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015). https://doi.org/10.1038/nature14236