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

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.

Files (49.3 kB)
Name Size
ljvmiranda921/gym-lattice-v0.1.0.zip
md5:4a87c6e5aba51083c994281b23b66bbc
49.3 kB
• 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.

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