In this guide, we’ll walk you through the steps to implement the SHAC algorithm, as described in the paper Accelerated Policy Learning with Parallel Differentiable Simulation. This will not only involve the installation but also example codes to train and test your policy using the provided differentiable simulation.

Installation Steps

To get started with SHAC, follow the installation steps carefully. Here’s a structured approach:

• Clone the repository:

git clone https://github.com/NVlabs/DiffRL.git --recursive

• Please make sure you are using one of the following configurations:
• Operating System: Ubuntu 16.04, 18.04, 20.04, 21.10, 22.04
• Python Versions: 3.7, 3.8
• Supported GPUs: TITAN X, RTX 1080, RTX 2080, RTX 3080, RTX 3090, RTX 3090 Ti

Setting Up the Environment

Next, we’ll create a virtual environment and install the necessary packages:

• In your project folder, create a virtual environment using Anaconda:

conda env create -f diffrl_conda.yml

• Activate the environment:

conda activate shac

• Install the dflex package:

cd dflex

pip install -e .

• Install rl_games from the forked repository:

cd externals/rl_games

pip install -e .

• Lastly, install the required version of protobuf:

pip install protobuf==3.20.0

Testing Your Installation

Once all installations are complete, you can run a test example. Switch to the examples folder and run:

python test_env.py --env AntEnv

If the console outputs Finish Successfully, congratulations! You’ve successfully set up the environment.

Training Your Model

Now it’s time to train your model. Use the following commands within the examples folder:

python train_shac.py --cfg .cfgshacant.yaml --logdir .logsAntshac

For convenience, we also provide a script to replicate results from the paper:

bash examples/train_script.sh

Running SHAC Methodology

If you wish to train specific environments with multiple seeds, execute:

python train_script.py --env Ant --algo shac --num-seeds 5

python train_script.py --env SNUHumanoid --algo shac --num-seeds 5

Using Baseline Algorithms

If you’re interested in training with baseline algorithms, like PPO, run:

python train_script.py --env Ant --algo ppo --num-seeds 5

Testing Your Model

To test the trained policy, input the policy checkpoint as follows:

python train_shac.py --cfg .cfgshacant.yaml --checkpoint .logsAntshacpolicy.pt --play [--render]

The –render flag enables video export in `.usd` format, saved in the examples/output folder.

Troubleshooting

If you encounter issues during installation or execution, here are some troubleshooting tips:

• Ensure you are operating in the correct Anaconda environment.
• Check your GPU compatibility and driver versions.
• Reinstall dependencies if any packages fail to install.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.