Deep-Active-Inference-for-Partially-Observable-MDPs

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Deep-Active-Inference-for-Partially-Observable-MDPs

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# Deep Active Inference for Partially Observable MDPs

This repository contains the code and results for the algorithms published in: van der Himst O., Lanillos P. (2020) [Deep Active Inference for Partially Observable MDPs](https://doi.org/10.1007/978-3-030-64919-7_8). In: Verbelen T., Lanillos P., Buckley C.L., De Boom C. (eds) Active Inference. IWAI 2020. Communications in Computer and Information Science, vol 1326. Springer, Cham. https://doi.org/10.1007/978-3-030-64919-7_8

Preprint: https://arxiv.org/abs/2009.03622

## Usage
The code contains implementations of an:
- Active inference agent designed for fully observable MDPs (ai_mdp_agent.py)
- Active inference agent designed for partially observable MDPs (ai_pomdp_agent.py)
- Deep Q agent designed for fully observable MDPs (dq_mdp_agent.py)
- Deep Q agent designed for partially observable MDPs (dq_pomdp_agent.py)

Use the following command line argument to run an (active inference POMDP) agent with the default parameters (the default being the parameters used in [1]):

```
	python ai_pomdp_agent.py
```
Note that when using the default parameters, the folders *logs*, *networks*, *results* and their subfolders are required,
 these folders may be empty (except when running default *ai_pomdp_agent.py*, which requires the file *vae_n32_end.pth* stored in the *pre_trained_vae* folder).

Specify custom parameters like:

```
	python ai_pomdp_agent.py device=cuda:0 load_pre_trained_vae=False pre_train_vae=True
```

Each implementation contains a function ```set_parameters(...)``` where its parameters are listed and described.

The agents were tested on the OpenAI gym's CartPole-v1 task. The MDP agents are able to do a variety of other (classic control) tasks, but at present the POMDP agents are restricted to the CartPole environment.

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