Sarah B, A Mira C and Eric F
)Reinforcement learning system as a submissive-dominant controller of VR experience-space.
- “Branching” of the state based on user head movement
- Machine trying to maximize the time the user stay in the VR environment
Deep Q Learning https://github.com/keon/deep-q-learning
Reinforcement learning in python: Theano + Keras
Source code originally written for playing CartPole game on OpenAI Gym
https://gym.openai.com/envs/CartPole-v1
“A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track”
We replaced CartPole game with our Unity VR system
- Form: head direction (coordinate in 3d-space), head movement (acceleration data)
- Dataset: collected from a human subject - 50+ trials
- When a human subject is bored, they push the “END” button.
- This resets the scene, the machine will retry to maximize the rewards
by collecting this data array:
- state 1 (VR gear readings)
- action (determined by the machine)
- state 2 (VR gear readings)
- rewards: for a unit time stay within the VR system, the system gets +1 reward; when terminated by the user, the system gets -10 penalty
The model selected 1 action from our discrete action space (306442=5760 possible actions) which determined each object's:
- visibility,
- position,
- size (scale), and
- angular orientation
Best record?
- Sarah: sound + training + documentation
- Alice: python(keras) + Unity + documentation
- Eric: Unity + VR + visuals