HW4: Value iteration¶
Due date: 2026-04-20 23:59.
Recommended Resources:
Recommended Resources:
- LaValle: Planning Algorithms, Ch 2,10 and 14, https://lavalle.pl/planning/
Value iteration¶
Lets give the recurrence equations for different variations of value iteration.
\text{a) }G_k^*(x_k) = \min_{u_k \in U(x_k)} \left\{\min_{\theta_k} \left\{ l(x_k, u_k,\theta_k) + G_{k+1}^*(x_{k+1}) \right\} \right\}.
\\\
\text{b) }G_k^*(x_k) = \min_{u_k \in U(x_k)} \left\{\max_{\theta_k} \left\{ l(x_k, u_k,\theta_k) + G_{k+1}^*(x_{k+1}) \right\} \right\}.
\\\
\text{c) }G_k^*(x_k) = \min_{u_k} \left\{ l(x_k, u_k) + G_{k+1}^*(x_{k+1}) \right\}.
\\\
\text{d) }C_k^{*}(x_k) = \min_{u_k^{-1}\in U^{-1}(x_k)} \left\{ l(x_{k-1}, u_{k-1}) + C_{k-1}^*(x_{k-1}) \right\}.
\\\
\text{e) }G_k^*(x_k) = \max_{u_k \in U(x_k)} \left\{ l(x_k, u_k) + \sum_{x_{k+1} \in X} G_{k+1}^*(x_{k+1})\, P(x_{k+1} \mid x_k, u_k) \right\}.
\\\
\text{f) }G_k^*(x_k) = \min_{u_k \in U(x_k)} \left\{ l(x_k, u_k) + \sum_{x_{k+1} \in X} G_{k+1}^*(x_{k+1})\, P(x_{k+1} \mid x_k, u_k) \right\}.
\\\
Python notebook¶
Download and open the following python notebook hw4_nb.ipynb
In valit_prob, each action u (graph edge) chosen by the robot, nature allows it to be chosen with probability p. The remaining possible k actions (graph edges) are each chosen with probability (1-p)/k.
Author¶
"Emil F. Awad"
Give feedback on this content
Comments about these exercises