[Recommended] i need help with 2 questions 3 and 4 NOT

SOLUTION AT Academic Writers Bay i need help with 2 questions 3 and 4 NOT 5. Robotics subject 3. Reinforcement Learning Reinforcements learning (RL) agents learn by taking state-dependent actions and experiencing reward arising from interaction with their environments. One method is to use a table-based Q-learning algorithm. Figure 1: The inverted pendulum problem Q-learning tables are discrete, but most real-world tasks involve systems that have continuous states and are controlled using continuous actions. With this in mind, consider how a table-based Q-learning algorithm could learn to balance an inverted pendulum (as shown in Fig. 1). To achieve this: (a) Describe a suitable reward function. [3 marks] (b) Describe a suitable choice of states and explain why they are appropriate. [3 marks] (c) Describe a suitable choice of actions and explain why they are appropriate and how they relate to the states discussed in part (a). [3 marks] (d) Discuss how an inverted pendulum task could be either an MDP or a POMDP. [2 marks] Question 3 continued … Question 3 continued (e) Discuss how simulated experience generated from a model within a RL agent can increase the speed with which the RL algorithm convergence. How can this assist finding a solution in the inverted pendulum task? [4 marks] (f) Dyna-Q algorithm is one such model-based approach to RL. Using high-level pseudo code in no more than 12 lines, describe the operation of the Dyna-Q algorithm and describe all its key terms. [5 marks] 4. State estimation (a) When building a full state feedback controller, why is if often necessary to use some form of state estimator? [3 marks] (b) The Luenberger observer is a deterministic state estimator. Draw its signal flow graph to illustrate its operation and explain the design and function of the Luenberger gain L. [3 marks] (c) The Kalman filter is a stochastic state estimator. Draw and compare a signal flow graph of the Kalman estimator with that of the Luenberger observer, illustrating all the Kalman estimator’s important components, including its noise sources. [4 marks] Question 4 continued … Question 4 continued (d) The Kalman filter iteratively computes 5 variables as illustrated below Write a short paragraph on each of the terms 1 – 5 to explain their meaning and function. [10 marks] 5. Gaussian processes Describe the main difference between using Gaussian Processes and Support Vector Machines in approximating linear functions. [20 marks] CLICK HERE TO GET A PROFESSIONAL WRITER TO WORK ON THIS PAPER AND OTHER SIMILAR PAPERS CLICK THE BUTTON TO MAKE YOUR ORDER