Train: REINFORCE CartPole
This tutorial will look at how to train an agent in SLM Lab, then use the automatically-saved model files to replay it in enjoy mode, using REINFORCE on CartPole.
REINFORCE is a very basic policy gradient algorithm, and we are training it on an easy environment. We will use the spec file at slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json, in particular the reinforce_cartpole spec, and run it in train mode. The lab command is:
python run_lab.py slm_lab/spec/benchmark/reinforce/reinforce_cartpole.json reinforce_cartpole train
This will run a Trial with 4 Sessions of different random seeds to average the results. Wait for it to run until completion, which should take about 10-20 minutes. Meanwhile, check the metrics logged in the terminal. In particular, the total_reward and its moving average total_reward_ma (with a window of 100 episodes) should climb up gradually.
REINFORCE successfully trains on CartPole when the total_reward_ma reaches close to the maximum of 200, although a score of over 100 will do for this tutorial.
When the trial completes, all the metrics, graphs and data will be saved to a timestamped folder, let's say data/reinforce_cartpole_2020_04_13_232521/. Among other things, SLM Lab also automatically saves the final and the best model files in the model folder. The model files can be used for easy playback in enjoy mode.
Next, we look at how to resume training, and to run a trained model in enjoy mode.
