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

Lester James Validad Miranda


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.1218143", 
  "title": "ljvmiranda921/gym-lattice: Major Release (v0.1.0)", 
  "issued": {
    "date-parts": [
      [
        2018, 
        4, 
        14
      ]
    ]
  }, 
  "abstract": "<p>Major Release (v0.1.0)</p>\n\n<p>Gym-lattice is an HP 2D Lattice Environment with a Gym-like API for the protein folding problem.</p>\n\n<p>This is a Python library that formulates Lau and Dill&#39;s (1989) hydrophobic-polar two-dimensional lattice model as a reinforcement learning problem. It follows OpenAI Gym&#39;s API, easing integration for reinforcement learning solutions.</p>\n\n<p>Features</p>\n\n<ul>\n\t<li>OpenAI Integration: uses Gym&#39;s API to ease compatibility to reinforcement learning solutions.</li>\n\t<li>Lattice 2D Environment: implements Dill and Lau&#39;s two-dimensional lattice as an RL problem.</li>\n\t<li>Command-line rendering environment: the method <code>render()</code> draws the chain on the command-line.</li>\n</ul>\n\n<p>Additionally, there is an option to set the penalty parameters for training the agent, this includes:</p>\n\n<ul>\n\t<li>Collision penalty (<code>collision_penalty</code>): accounts for the time whenever the agent decides to assign a molecule to an already-occupied space; and</li>\n\t<li>Trap penalty (<code>trap_penalty</code>): induces heavy deductions whenever the agent traps itself and cannot accomplish the task anymore.</li>\n</ul>\n\n<p>Tests</p>\n\n<ul>\n\t<li>Error-handling: all public-facing methods now use <code>Exceptions</code> instead of <code>asserts</code> when catching errors.</li>\n\t<li>Testing with pytest and tox: unit-testing is being done with <code>pytest</code> and <code>tox</code>.</li>\n</ul>", 
  "author": [
    {
      "family": "Lester James Validad Miranda"
    }
  ], 
  "version": "v0.1.0", 
  "type": "article", 
  "id": "1218143"
}
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