A reinforcement learning module
The Reinforcement module aims to provide simple implementations for various reinforcement learning algorithms. The module tries to be agnostic about its use cases, but implements different solutions for policy selection, value- and q-function approximations as well as different agents for reinforcement learning algorithms.
The project is in its early stage and currently only provides an n-step temporal difference learning agent. The main purpose of the project is to facilitate my own understanding of reinforcement learning, with no particular application in mind.
The module is organises in 3 main parts. Policies, reward functions and agents, each providing necessary components to construct a reinforcement learning agent. Components should have a low dependency amongst each other and share a simple common interface to facilitate modular construction of agents.
This module contains the actual agents implementing the reinforcement learning algorithm using a policy component and a reward function component. Currently only a n-step temporal difference agent is implemented.
This module contains action selection policies used by reinforcement learning agents. Available policies: epsilon greedy; normalized epsilon greedy.
This module contains implementations of reward functions, which are used by reinforcement learning agents. Available reward functions: value table, q table, q neural network
Reinforcement also contains neural network implementation which can be used as non-linear reward function approximiations. Currently there are 2 regression models implemented, one using Keras and one using pure Tensorflow.
This software is crafted using Test Driven Development and tries to adhere to the SOLID principle as far as it lies in the abilities of the author.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|reinforcement-1.0.6-py3-none-any.whl (11.1 kB) Copy SHA256 hash SHA256||Wheel||py3||Apr 7, 2018|
|reinforcement-1.0.6.tar.gz (7.6 kB) Copy SHA256 hash SHA256||Source||None||Apr 7, 2018|