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ljvmiranda921/gym-lattice: Major Release (v0.1.0)

Lester James Validad Miranda


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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Lester James Validad Miranda</dc:creator>
  <dc:date>2018-04-14</dc:date>
  <dc: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 of asserts when catching errors.
	Testing with pytest and tox: unit-testing is being done with pytest and tox.
</dc:description>
  <dc:identifier>https://zenodo.org/record/1218143</dc:identifier>
  <dc:identifier>10.5281/zenodo.1218143</dc:identifier>
  <dc:identifier>oai:zenodo.org:1218143</dc:identifier>
  <dc:relation>url:https://github.com/ljvmiranda921/gym-lattice/tree/v0.1.0</dc:relation>
  <dc:relation>doi:10.5281/zenodo.1214967</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:subject>reinforcement learning</dc:subject>
  <dc:subject>protein folding problem</dc:subject>
  <dc:subject>open source software</dc:subject>
  <dc:title>ljvmiranda921/gym-lattice: Major Release (v0.1.0)</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>
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