(C-x means ctrl+x, M-x means alt+x)
The default prefix is C-b. If you (or your muscle memory) prefer C-a, you need to add this to ~/.tmux.conf
:
# Simple: | |
# a --> b | |
# --> c --> d | |
# --> d | |
graph1 = { | |
"a": ["b", "c", "d"], | |
"b": [], | |
"c": ["d"], | |
"d": [] | |
} |
. | |
├── actions | |
├── stores | |
├── views | |
│ ├── Anonymous | |
│ │ ├── __tests__ | |
│ │ ├── views | |
│ │ │ ├── Home | |
│ │ │ │ ├── __tests__ | |
│ │ │ │ └── Handler.js |
license: MIT | |
height: 420 |
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
# coding: utf-8 | |
# Imports | |
import os | |
import cPickle | |
import numpy as np | |
import theano | |
import theano.tensor as T |
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: |
# 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): |