Solving Mazes with Reinforcement Learning - Part 10 - Building the Intrinsic Curiosity Module

This is part 10 of a video series on solving Mujoco mazes with Pytorch, SAC, Curriculum Learning, and Intrinsic Curiosity. In this video, we'll be building the intrinsic curiosity module and implementing curriculum learning. As promised, here's the block of 1s and 0s for the large maze.
LARGE_MAZE = [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1],
[1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1],
[1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1],
[1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1],
[1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
[1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1],
[1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
For more information on the environment - robotics.faram...
I'm not an AI practitioner, just a fan of AI posting my own personal projects. For a great intro course to reinforcement learning, I recommend checking out Phil Tabor's Udemy courses on the topic.
Finally, if you're new to Pytorch and deep learning in general, Mike Cohen's course on deep learning is the best place I can think of to start.
www.udemy.com/...

[@WilliamChen-pp3qs]

why are we only using the forward model instead of both forward and inverse model of ICM?