Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https://arxiv.org/pdf/1707.06347.pdf).
I finally fixed what was wrong with the gradient descent step, using previous log-prob from rollout batches. At least ppo.py is fixed, the rest is going to be corrected as well very soon.
In the following example I was not patient enough to wait for million iterations, I just wanted to check if the model is properly learning:
Progress of DPPO (4 agents) [TODO]
The structure of this code is based on https://github.com/ikostrikov/pytorch-a3c.
Hyperparameters and loss computation has been taken from https://github.com/openai/baselines