stEVE_rl

This is a reinforcement learning library implemented for the use with the stEVE framework. Albeit it can be used with any environment implementing the Farama gymnasium interface.

This framework implements the Soft-Actor-Critic method using pytorch.

The emphasis of this library lies on parallelisation of working agents, which perform episodes in the simulation and a single training agent utilizing one GPU. This is specifically helpful for computational intensive simulations.

Getting Started

1. Setup stEVE (including Sofa)
2. Install stEVE_rl package

   python3 -m pip install -e .
   

3. Test the installation

   python3 examples/function_check.py
   

How to use

1. Design your Neural Network using network components (e.g. MLP, CNN, LSTM). This will define the hidden layers
2. Bundle them in a network structure (e.g. Q-Network, Gaussian-Policy-Network). This will define the input, output and connection between the components.
3. Define Optimizer and Scheduler for the neural networks.
4. Bundle all of them in a neural network model (specific to each algorithm).
5. Define the algorithm (e.g. SAC).
6. Define a Replay Buffer.
7. Define an Agent.
8. Write your training loop or use one of the runners.

Have a look at the example folder! More sophisticated usage examples can be found in stEVE_training.