rlworkgroup/garage

Split GaussianMLPModel into several variants

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#587 opened on Mar 19, 2019

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Description

The current GaussianMLPModel actually represents several architecture variants, which makes it difficult to use and to test. We should split it into several variants based on architecture (and possibly parameterization)

description mean std_param
fixed variance MLP1 parameter (trainable or not)
split network MLP1 MLP2
shared network/multi-headed MLP1/head1 MLP1/head2

Contributor guide

Research direction
Study the current GaussianMLPModel class to identify the three architecture variants (fixed variance, split network, shared network). Then design and implement separate classes for each variant, ensuring backward compatibility and adding tests.
Tech stack
python
Domain
machine learningai
Issue type
Refactor
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonPyTorch or TensorFlow (reinforcement learning)
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
40