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#General_Deep_Q_RL

Introduction

This package provides a Lasagne/Theano-based implementation for a general deep Q-learning algorithm. It is based on the original deep Q learning algorithm described in :

Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533.

Contrary to the original code, this package provides a more general framework where observations are made up of any number of elements : scalars, vectors and frames (instead of one type of frame only in the above mentionned paper). At each time step, an action is taken by the agent and one observation is gathered. The belief state on which the agent is based to build the Q function is made up of any length history of each element provided in the observation.

Dependencies

• A reasonably modern NVIDIA GPU
• OpenCV
• Theano (https://github.com/Theano/Theano)
• Lasagne (https://github.com/Lasagne/Lasagne

How to use it - Examples

Two storage devices example

This second example is slightly more complex and realistic. It simulates the operation of a micro-grid (such as a smart home for instance) that is not connected to the main utility grid (off-grid) and that is provided with PV panels, batteries and hydrogen storage. The battery has the advantage that it is not limited in instaneous power that it can provide or store. The hydrogen storage has the advantage that is can store very large quantity of energy. Details can be found in the MG_two_storage_devices_env.py.

python run_MG_two_storage_devices