Distributed Tensorflow Implementation of a3c from Google Deepmind.
Implementation is on Tensorflow 1.0
http://arxiv.org/abs/1602.01783
"We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers." Form Paper
The implementation is based on miyosuda git. https://github.com/miyosuda/async_deep_reinforce
The core implementation is almost same to miyosuda ones. The parts for running with Distributed Tensorflow are changed.
./auto_run.sh
You need to set your hostname and port number in a3c.py code. The number of parameter servers and workers can be set in auto_run.sh script file.
Almost Setting values are same to miyosuda ones except the number of workers. The Number of worker is 14, and the number of parameter server is 7.
The experiment is Pong game with the number of worker as above ones. This one runs with just CPU. The CPU and memory of server are Xeon E7-8880 v4 x 4 (176 threaing with hyper-threading) and 1TB respectively (cpu and memory are not fully used. About 50% of CPU and 20GB Memory are used). As above result, about 800~900 steps run per seconds (that is better than 980ti performance in miyosuda git), and score is saturated at about 35M steps.
