Predicting future in gridworld simulator

This code reimplements the Prednet model (mostly copy & paste): [Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning] [github], in gridworld sequence data.

About the code

I adopt the source implementations mostly, except:

• Using tf.contrib.keras (it's all the same if you use 'TF' backends in Keras)
• Bug fixed. (extrap_start_time, see [this])
• New data_generator in Gridworld Simulator. The grid-world simulator simulates a free energy loss physics in a 2D scene.
• Add roi-loss.

All the hyper-parameters are in train.py.

Run:

python train.py

Evaluate:

python evaluate.py

Requirements:

• Python 2
• Tensorflow 1.1 (keras in tf.contrib)
• Pygame
• Numpy

MISC:

I completed this code when I was an intern at Horizon Robotics. I will greatly thank the paper of William Lotter in ICLR 2017, and his implementations:PredNet. Also greately thanks my mentor Penghong Lin, and other colleagues (Lisen Mu, Jingchu Liu and Henglai Wei) for helpful discussion.