PyTorch implementation of batch D3PG, which uses n-step Bellman update and parallel experience sampling. The Detail of this algorithm can be found in this paper:

Barth-Maron, Gabriel, et al. "Distributed distributional deterministic policy gradients." arXiv preprint arXiv:1804.08617 (2018).

• pytorch 1.4.0
• tensorboard
• numpy
• tqdm
• gym
• baselines
• pybullet (optional)

You can use the provided requirements.txt file to install necessary dependencies.

$ pip install -r requirements.txt

For example, to train a d3pg agent using 12 processes for pybullet ant locomotion task as follows:

$ python train.py --task-id=AntBulletEnv-v0 --num-processes=12 --num-env-steps=5000000

You can also monitor the training process and perform hyper-parameters tuning using tensorboard:

$ tensorboard --logdir=log