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I described the individual components in detail, immediately followed by their implementation.
Million dollar pyramid gif how to#
Now, two years later, I implemented DQN, learned a lot about neural networks and Reinforcement Learning and therefore decided to write a hopefully comprehensive tutorial on how to make it work for other people who, like me, are fascinated by Deep Learning.ĭeep Q-Learning needs several components to work such as an environment which the agent can explore and learn from, preprocessing of the frames of the Atari games, two convolutional neural networks, an answer to the exploration-exploitation dilemma (e-greedy), rules to update the neural networks’ parameters, error clipping and a buffer called Replay Memory where past game transitions are stored in and drawn from when learning. I was immediately fascinated and promised myself that I would try to recreate this result once I could afford to dedicate some time to it. in Theoretical Astrophysics when I first watched DeepMind’s video showing a DQN agent learning to play the game Breakout and discovering that it could “dig a tunnel” around the side that allows the ball to hit blocks by bouncing behind the wall. Now, five years later, there are even more advanced reinforcement learning algorithms but in my opinion, Deep Q-Learning is still an extremely impressive method that is well worth studying (and more importantly getting to work). The company has since been one of the leading institutions advancing Deep Learning research and a later article discussing DQN has been published in Nature. No wonder DeepMind was bought by Google for 500 Million Dollars. The agent even surpasses human expert players in some of those games! This is an astonishing result because previously “AIs” used to be limited to one single game, for instance, chess, whereas in this case the types and contents of the games in the Arcade Learning Environment vary significantly and yet no adjustment of the architecture, learning algorithm or hyperparameters is needed.
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In 2013 a London ba sed startup called DeepMind published a groundbreaking paper called Playing Atari with Deep Reinforcement Learning on arXiv: The authors presented a variant of Reinforcement Learning called Deep Q-Learning that is able to successfully learn control policies for different Atari 2600 games receiving only screen pixels as input and a reward when the game score changes.