name: plan-mega-review
version: 2.0.0
description: |
The most thorough plan review possible. Three modes: SCOPE EXPANSION (dream big,
build the cathedral), HOLD SCOPE (review what's here with maximum rigor), and
SCOPE REDUCTION (strip to essentials). Context-dependent defaults, but when the
user says EXPANSION — go full send. Challenges premises, maps every failure mode,
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| # From udacity Machine Learning Nanodegree course | |
| import numpy as np | |
| # Define sigmoid function | |
| def sigmoid(x): | |
| return 1/(1+np.exp(-x)) | |
| # Derivative of the sigmoid function | |
| def sigmoid_derivative(x): |
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| from keras import backend as K, initializers, regularizers, constraints | |
| from keras.engine.topology import Layer | |
| def dot_product(x, kernel): | |
| """ | |
| Wrapper for dot product operation, in order to be compatible with both | |
| Theano and Tensorflow | |
| Args: |
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| # coding: utf-8 | |
| # Imports | |
| import os | |
| import cPickle | |
| import numpy as np | |
| import theano | |
| import theano.tensor as T |
Used dueling network architecture with Q-learning, as outlined in this paper:
Dueling Network Architectures for Deep Reinforcement Learning
Ziyu Wang, Tom Schaul, Matteo Hessel, Hado van Hasselt, Marc Lanctot, Nando de Freitas
http://arxiv.org/abs/1511.06581
Command line:
python duel.py CartPole-v0 --gamma 0.995
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| license: MIT | |
| height: 420 |
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| . | |
| ├── actions | |
| ├── stores | |
| ├── views | |
| │ ├── Anonymous | |
| │ │ ├── __tests__ | |
| │ │ ├── views | |
| │ │ │ ├── Home | |
| │ │ │ │ ├── __tests__ | |
| │ │ │ │ └── Handler.js |
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| # Simple: | |
| # a --> b | |
| # --> c --> d | |
| # --> d | |
| graph1 = { | |
| "a": ["b", "c", "d"], | |
| "b": [], | |
| "c": ["d"], | |
| "d": [] | |
| } |
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