Experiments

The full implementation of the A3C algorithm can be downloaded from our GitHub repository (https://github.com/PacktPublishing/Python-Reinforcement-Learning-Projects). There are three environments in our implementation we can test. The first one is the special game, demo, introduced in Chapter 7, Playing Atari Games. For this game, A3C only needs to launch two agents to achieve good performance. Run the following command in the src folder:

python3 train.py -w 2 -e demo

The first argument, -w, or --num_workers, indicates the number of launched agents. The second argument, -e, or --env, specifies the environment, for example, demo. The other two environments are Atari and Minecraft. For Atari games, A3C requires at least 8 agents running in parallel. Typically, launching 16 agents can achieve better performance:

python3 train.py -w 8 -e Breakout

For Breakout, A3C takes about 2-3 hours to achieve a score of 300. If you have a decent PC with more than 8 cores, it is better to test it...