DQN for Wireless Control

This projects provides a modular python-based deep reinforcement learning implementation as well as an example application in wireless network control.

The package currently implements both the canonnical table-based Q learning agent in rl.qtable.QAgent and the more recent neuron-network-based version, a.k.a deep q network (DQN), using Theano and Lasagne in rl.qnn_theano.QAgentNN.

A set of additional agent features have also been implemented as Mixin classes in rl.mixin.

1. In non-Markovian environments, PhiMixin can be used with either type of agents to stack the historical observation into a augmented observation vector.
2. For a better exploration and exploitation tradeoff, we implemented a anealing mixin class in AnealMixin for gradually decrease exploration rate.
3. When a environment model is available, DynaMixin can be used to incorporate planning into the learnign process.

For testing, we provide a simple maze example in rl.simple_env.SimpleMaze.

The wireless networking application is dynamic online base station sleeping control.