Lab Blog

Blag Weeks 8-11: I Heart Gauss

The team gained a greater appreciation for Johann Carl Friedrich in the implementation of Gaussian Process Regression with basis functions. GPR was chosen to replace LWPR because of LWPR’s failure to generalize outside of the state space already explored. The inclusion of basis functions in GPR will allow global semi-parametric modeling of system dynamics with Gaussian Processes modeling the local residuals. We adopted the code and book from Gaussian Processes for Machine Learning by Carl Edward Rasmussen and Chris Williams.

Week 6: Working Memory

This week saw a big change of direction for the project. With the higher capacity working memory model completely debugged, I expected the beginning of this week to be data analysis, before moving on to a bigger, better model. But the results we saw made us reconsider our entire approach to the problem of serial recall phenomena.

The working memory content never surpassed two items, even with the theoretical capacity of four. How did this happen? It was due to the probability of spontaneously forgetting any stimulus. Consider the following situation:

Blag Weeks 6, 7: A Hill Too Steep to Climb

Sometimes models don’t generalize well. The team’s path met a gradient too great to climb: LWPR could not generalize the local models (built with receptive fields) to broader regions of the state space for use in the controller. This makes sense, in hindsight, because LWPR is an efficient high-dimensional function approximation approach. It performed worse than regularized linear regression on the forward model identification task, with and without additional basis functions.

Working Memory: Week 5

I got a head start on this week by finishing up a draft of the code for a model with a higher capacity working memory as well as the ability to forget.

Blag Weeks 4, 5: The Implementation Battle Progresses

The Lifelong Learning team is closing in on the SysID-Control-Simulation problem.

Working Memory: Week 3

This past week I worked on an my implementation of Q-learning, a method of reinforcement learning which converges quickly and does not depend on any knowledge of the environment. I plan to use Q-learning to model working memory, where the agent learns whether to replace or retain the contents of working memory based on past experience.

Blag Week 3: Attacking the Problem

Week 3 saw the first real combat with the software and control problems, and broader computer lab remodeling work. Ten shiny new monitors arrived, and much fun was had playing with setting them up.

Blag Week 2: Define the Machine

“I was born not knowing and have had only a little time to change that here and there.” - Richard Feynman

Having been inspired by Paul Ruvolo’s work with “Diego San”, the three feckless “researchers” set down the path of control theory and trajectory optimization, with an eventual goal of extending the lifelong learning framework to fault-tolerant control.

Eye-Helper Day 1 (06/02/2014)

Day 1

Blahg Week 1: Enter the Machine....(Learning)

“If learning is the answer, what is the question?” - Yoav Shoham, Kevin Leyton-Brown, Multiagent Systems.

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