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昨天晚上,我把……里的……全部看了一遍.从现在到以前的全部看了一遍.看着你那……啦 心里好难过 越往后边看心里越难过 也越来越想你 想着你那熟悉的表情 想着你那熟悉的微笑 想着你那熟悉的发香 …… 我不禁想起了从前 从前那段美好的日子 那段美好的7个月 现在 见了面 如同陌生人 在8月份那个暑假 你离开了我 我安静的接受了 我已经习惯不去阻止你 我想挽留 可我却为什么了点了头 我连我自己都不懂我 ……我很舍不得,我非常遗憾 在……最大的遗憾 我不知道现在要不要 我不想留下遗憾 我在离开你的这段日子里 没有一天不想你 我不想失去你 能不能从新开始 从新认识彼此 好吗?ConvNetJS Deep Q Learning Reinforcement Learning with Neural Network demo
Deep Q Learning Demo
Description
This demo follows the description of the Deep Q Learning algorithm described in
a paper from NIPS 2013 Deep Learning Workshop from DeepMind. The paper is a nice demo of a fairly
standard (model-free) Reinforcement Learning algorithm (Q Learning) learning to play Atari games.
In this demo, instead of Atari games, we'll start out with something more simple:
a 2D agent that has 9 eyes pointing in different angles ahead and every eye senses 3 values
along its direction (up to a certain maximum visibility distance): distance to a wall, distance to
a green thing, or distance to a red thing. The agent navigates by using one of 5 actions that turn
it different angles. The red things are apples and the agent gets reward for eating them. The green
things are poison and the agent gets negative reward for eating them. The training takes a few tens
of minutes with current parameter settings.
Over time, the agent learns to avoid states that lead to states with low rewards, and picks actions
that lead to better states instead.
Q-Learner full specification and options
The textfield below gets eval()'d to produce the Q-learner for this demo. This allows you to fiddle with
various parameters and settings and also shows how you can use the API for your own purposes.
All of these settings are optional but are listed to give an idea of possibilities.
Feel free to change things around and hit reload! Documentation for all
options is the paper linked to above, and there are also
comments for every option in the source code javascript file.
var num_inputs = 27; // 9 eyes, each sees 3 numbers (wall, green, red thing proximity)
var num_actions = 5; // 5 possible angles agent can turn
var temporal_window = 1; // amount of temporal memory. 0 = agent lives in-the-moment :)
var network_size = num_inputs*temporal_window + num_actions*temporal_window + num_
// the value function network computes a value of taking any of the possible actions
// given an input state. Here we specify one explicitly the hard way
// but user could also equivalently instead use opt.hidden_layer_sizes = [20,20]
// to just insert simple relu hidden layers.
var layer_defs = [];
layer_defs.push({type:'input', out_sx:1, out_sy:1, out_depth:network_size});
layer_defs.push({type:'fc', num_neurons: 50, activation:'relu'});
layer_defs.push({type:'fc', num_neurons: 50, activation:'relu'});
layer_defs.push({type:'regression', num_neurons:num_actions});
// options for the Temporal Difference learner that trains the above net
// by backpropping the temporal difference learning rule.
var tdtrainer_options = {learning_rate:0.001, momentum:0.0, batch_size:64, l2_decay:0.01};
var opt = {};
opt.temporal_window = temporal_
opt.experience_size = 30000;
opt.start_learn_threshold = 1000;
opt.gamma = 0.7;
opt.learning_steps_total = 200000;
opt.learning_steps_burnin = 3000;
opt.epsilon_min = 0.05;
opt.epsilon_test_time = 0.05;
opt.layer_defs = layer_
opt.tdtrainer_options = tdtrainer_
var brain = new deepqlearn.Brain(num_inputs, num_actions, opt); // woohoo
Q-Learner API
It's very simple to use deeqlearn.Brain: Initialize your network:
var brain = new deepqlearn.Brain(num_inputs, num_actions);
And to train it proceed in loops as follows:
var action = brain.forward(array_with_num_inputs_numbers);
// action is a number in [0, num_actions) telling index of the action the agent chooses
// here, apply the action on environment and observe some reward. Finally, communicate it:
brain.backward(reward); // <-- learning magic happens here
That's it! Let the agent learn over time (it will take opt.learning_steps_total), and it
will only get better and better at accumulating reward as it learns. Note that the agent will still take
random actions with probability opt.epsilon_min even once it's fully trained.
To completely disable this randomness, or change it, you can disable the learning and set epsilon_test_time to 0:
brain.epsilon_test_time = 0.0; // don't make any random choices, ever
brain.learning =
var action = brain.forward(array_with_num_inputs_numbers); // get optimal action from learned policy
State Visualizations
Left: Current input state (quite a useless thing to look at). Right: Average reward over time (this should go up as agent becomes better on average at collecting rewards)
(Takes ~10 minutes to train with current settings. If you're impatient, scroll down and load an example pre-trained network from pre-filled JSON)
Go very fast
Go normal speed
Start Learning
Stop Learning
You can save and load a network from JSON here. Note that the textfield is prefilled with a
pretrained network that works reasonable well, if you're impatient to let yours train enough.
Just hit the load button!
Save network to JSON
Load network from JSON
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