Published April 14, 2018
| Version v0.1.0
Software
Open
ljvmiranda921/gym-lattice: Major Release (v0.1.0)
Description
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 ofasserts
when catching errors. - Testing with pytest and tox: unit-testing is being done with
pytest
andtox
.
Files
ljvmiranda921/gym-lattice-v0.1.0.zip
Files
(49.3 kB)
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Additional details
Related works
- Is supplement to
- https://github.com/ljvmiranda921/gym-lattice/tree/v0.1.0 (URL)
References
- Lau, K.F. and Dill, K.A., 1989. A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22(10), pp.3986-3997.