Date | Topic | Material | Recommended exercises | ||
1 | Feb 8 11-13 hours |
Discrete time control dynamic programming Bellman equation |
Bertsekas 2-5, 13-14, 18, 21-32 | Bertsekas 1.1 a and b, 1.2 | |
2 | Feb 9 11-13 hours |
Continuous time control Hamilton-Jacobi-Bellman Equation Pontryagin Minimum Principle Stochastic optimal control |
Kappen ICML tutorial 1.2, 1.3, 1.4 |
extra exercise 1, 2a,b Bertsekas 3.2 | |
3 | Feb 21 10-13 hours |
Dual control: the problem of joint inference and control Path integral control theory |
Kappen ICML tutorial 1.5,1.6, 1.7 | extra exercise 2c, 3 | |
4 | Feb 22 10-13 hours |
Stochastic optimal control Path integral control theory |
Kappen ICML tutorial 1.7 |
extra exercise 4,5
Matlab code for n joint problem Here is a directory of matlab files, which allows you to run and inspect the variational approximation for the n joint stochastic control problem as discussed in the tutorial text section 1.6.7. Type tar xvf njoints.tar to unpack the directory and simply run file1.m. In file1.m you can select demo1 (3 joint arm) or demo2 (10 joint arm). You can also try larger n but be sure to adjust eta for the smoothing of the variational fixed point equations. You can compare the results with exact cmputation (only recommendable for 2 joints) by setting METHOD='exact'. There is also an implementation of importance sampling (does not work very well) and Metropolis Hastings sampling (works nice, but not as stable as the variational approximation). |