10.5281/zenodo.1218143
https://zenodo.org/records/1218143
oai:zenodo.org:1218143
Lester James Validad Miranda
Lester James Validad Miranda
0000-0002-7872-6464
Waseda University
ljvmiranda921/gym-lattice: Major Release (v0.1.0)
Zenodo
2018
reinforcement learning
protein folding problem
open source software
2018-04-14
https://github.com/ljvmiranda921/gym-lattice/tree/v0.1.0
10.5281/zenodo.1214967
v0.1.0
Other (Open)
Major Release (v0.1.0)
Gym-lattice is an HP 2D Lattice Environment with a Gym-like API for the protein folding problem.
This is a Python library that formulates Lau and Dill's (1989) hydrophobic-polar two-dimensional lattice model as a reinforcement learning problem. It follows OpenAI Gym's API, easing integration for reinforcement learning solutions.
Features
OpenAI Integration: uses Gym's API to ease compatibility to reinforcement learning solutions.
Lattice 2D Environment: implements Dill and Lau's two-dimensional lattice as an RL problem.
Command-line rendering environment: the method render() draws the chain on the command-line.
Additionally, there is an option to set the penalty parameters for training the agent, this includes:
Collision penalty (collision_penalty): accounts for the time whenever the agent decides to assign a molecule to an already-occupied space; and
Trap penalty (trap_penalty): induces heavy deductions whenever the agent traps itself and cannot accomplish the task anymore.
Tests
Error-handling: all public-facing methods now use Exceptions instead of asserts when catching errors.
Testing with pytest and tox: unit-testing is being done with pytest and tox.