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Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

            

Figure 1: Model predictions learned by D3G in a Gridworld, InvertedPendulum-v2, and Reacher-v2.


Official PyTorch implementation of Deep Deterministic Dynamics Gradients. Code is based heavily on the Twin Delayed DDPG implementation. For research purpose only. Support and/or new releases may be limited.

Setup

Clone the repo:

git clone https://github.com/uber-research/D3G.git && cd D3G

We use Python 3.6.2. Requirements for D3G can be installed by running:

pip install -r D3G/requirements.txt

Running toy QSS problems

The stochastic action results can be reproduced by running:

cd toy_problems/stochastic_actions && python model_gridworld.py --stochasticity {rand}

The windy cliffworld results can be reproduced by running:

cd toy_problems/windy_cliffworld && python model_gridworld.py 

The redundant action results can be reproduced by running:

cd toy_problems/redundant_actions && python model_gridworld.py

The shuffled action results can be reproduced by running:

cd toy_problems/shuffled_actions && python model_gridworld.py && python model_gridworld.py --shuffled

Running D3G

The paper results can be reproduced by running:

cd D3G && ./mujoco_experiments/run_D3G_experiments.sh

The model predictions for Reacher-v2 and InvertedPendulum-v2 can be visualized. To see this, run:

cd D3G && python main.py --policy D3G --env Reacher-v2 --visualize

Running Learning from Observation

The paper results can be reproduced by running:

cd D3G && ./lfo_experiments/run_D3G_experiments

The model predictions for Reacher-v2 and InvertedPendulum-v2 can be visualized here too. To see this, run:

cd D3G && python learn_from_observation.py --policy D3G --env Reacher-v2 --visualize

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Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

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