Beating Atari Games with OpenAI's Evolutionary Strategies • Filestack Blog

Last month, Filestack sponsored an AI meetup wherein I presented a brief introduction to reinforcement learning and evolutionary strategies. Beforehand, I had promised code examples showing how to beat Atari games using PyTorch. In reality, I did not have time for that kind of side project and so I found some other examples of training agents to play Flappy Bird using Keras, which were entertaining but not complete enough for me to recommend as a springboard for further exploration. Luckily, I recently found some time to develop the promised training scripts. Therefore, I would like to provide an in-depth look of how we can use the PyTorch-ES suite for training reinforcement agents in a variety of environments, including Atari games and OpenAI Gym simulations. In deep reinforcement learning that uses the Q-learning algorithm, which has become very popular, training an intelligent agent includes distinct phases for "observation" and "learning".