Modular Deep RL infrastructure in PyTorch.
Currently implemented algorithms include PPO and PPOC (Option-Critic trained with Proximal Policy Optimization).
To train, first write a config file and save to configs/ppo or configs/ppoc. A config file specifies parameters in config objects (found in core/config.py) for each component of the model and experiment, such as optimization hyperparameters, network architectures, environments, logging behavior, etc. See example config at configs/ppo/lightbot.py
The specified configuration and algorithm can then be run by calling:
python train.py --config [config file name] --algo [ppo or ppoc]
See train.py for other options.
To evaluate a pre-trained model, run:
python evaluate.py --model_dir [logging directory] --episode [episode to load checkpoint from] --n_eval_steps [number of steps to evaluate]
Data from the evaluated model is saved to [logging directory]/evaluate/