Policy Generation for Mobile Robot in obstacle environment using Policy Iteration , Generalised Policy Iteration , and Value Iteration in both Deterministic , and Stochastic Models. Here process is assumed to be Markov Decision Process , and the problem is solved using Dynamic Programming .
In this programing problem I am going to solve the problem explain in Figure 14.2 of the book “Probabilistic Robotics” using Dynamic Programing.
The robot can move in 8 directions (4 straight + 4 diagonal). The robot has two model: a) Deterministic model, that always executes movements perfectly. b) Stochastic model, that has a 20% probability of moving +/-45degrees from the commanded move. (1 means occupied and 0 means free). The reward of hitting obstacle is -50.0 . Reward for any other movement that does not end up at goal is -1.0. The reward for reaching the goal is 100.0. The goal location is at W(8,11) Use gamma =0.95.
Requirement: To generate the optimal policy for the robot using the following algorithms:
1) Policy iteration (algorithm on page 80 Sutton and Barto)
2) Value Iteration (algorithm on page 83 Sutton and Barto)
3) Generalized Policy Iteration
