Topic  Material  Recommended exercises  
1  Discrete time control dynamic programming Bellman equation 
Bertsekas 25, 1314, 18, 2132 (2nd ed.) Bertsekas 25, 1012, 1627, 3032 (1nd ed.) Kappen ICML tutorial 1.2 
Bertsekas 1.1 a and b, 1.2  
2 
Continuous time control HamiltonJacobiBellman Equation Pontryagin Minimum Principle Stochastic differential equations 
Kappen ICML tutorial 1.3 
extra exercise 1, 2a,b 

3 
Stochastic optimal control LQ examples, Portfolio management Path integral control theory 
Kappen ICML tutorial 1.4, 1.6  extra exercise 2c, 3  
4  Path integral control theory Delayed choice example Importance sampling Laplace approximation How to control a device? KL control theory and link to path integrals Multiagent systems Stationary KL control (Dual control: the problem of joint inference and control) (Risk sensitive control) (Numerical examples (particle in a box, Darts, N joint arm, Coordination of agents) 
Kappen ICML tutorial 1.6, 1.7, (1.5) Theodorou et al., AISTATS 2010 Mensink et al., ECAI 2010 van den Broek et al., JAIR 2008 van den Broek et al., UAI 2010 Kappen et al, arxiv:0901.0633 2009 
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). 