Atari game shooting blocks code#

The experiment proves that effective machine learning is possible on various architectures, including more common CPUs. To make the learning process more effective, we used an innovative multi-node infrastructure based on Xeon processors provided by Intel. Similar experiments optimizing the training time of Atari games have been conducted by Adam Stooke and Pieter Abbeel from UC Berkeley among others, including OpenAI and Uber.

Atari game shooting blocks code#

We provided the code and technical details on arXiv, GitHub and in a blog post, so that others can easily recreate the results. The results of the parallelization experiment conducted by deepesense.ai were impressive – the algorithm required only 20 minutes to master Atari video games, a vast improvement over the approximately one week required in the original experiments done by DeepMind. From a single image the RL agent can learn about the current positions of game objects, but by combining the current image with a few that preceded it, the deep neural network is able to learn not only positions, but also the game’s physical characteristics, such as speed at which objects are moving. The reinforcement learning agent learns only from visual input, and has access to only the same information given to human players. Related: Five trends for business to surf the big data wave Cutting the time of deep reinforcement learningĭeepMind’s work inspired various implementations and modifications of the base algorithm including high-quality open-source implementations of reinforcement learning algorithms presented in Tensorpack and Baselines. While the whole process may sound like a like bunch of scientists having fun at work, playing Atari with deep reinforcement learning is a great way to evaluate a learning model. On a more sobering note, if someone had a problem understanding the rules of “Space invaders”, would you let him drive your car? By 2016 DeepMind had cut the time to 24 hours by improving the algorithm. In 2013 the learning algorithm needed a whole week of uninterrupted training in an arcade learning environment to reach superhuman levels in classics like Breakout (knocking out a wall of colorful bricks with a ball) or Space Invaders (shooting out alien invaders with a mobile laser cannon). Thus the algorithm had to learn the rules of the game and find the most effective tactics by itself to maximize the long-term rewards it earned. There were no further hints or suggestions.

In the case of the Atari 2600 game, the only indicator of success was the points the artificial intelligence earned. In the near future it may be driving an autonomous car. Now, with reinforcement learning, such a system has become a pillar of teaching computers to perform more sophisticated tasks, such as beating human champions in the game Go. A well-designed system of rewards is essential in human education.