In this project, is available a practical Demonstration of three different Reinforcement Learning Approaches which can be used in order to solve the Open AI Gym Mountain Car Problem. The Mountain Car problem is a classic Reinforcement Learning exercise. In this scenario, the agent (a car) is stuck in a valley and aims to drive up to the top of a hill by optimising it’s velocity and position (continuous state space). In this exercise, three different approaches have been considered in order to solve this problem: using tabular Q-learning, approximating the tabular representation using RBF and learning the weights of the RBF approximation on-line using Q-learning.
Tabular approximation, RBF Tabular approximation and Q-learning RBF approximation in action
In this section, is available a video demonstration of the 3 proposed methods in action in order to solve the mountain car problem. As shown in the following video, the Q-learning RBF approximation was the method which performed best in order to solve this task with the less amount of steps as possible.
RBF Q-learning Approximation optimised Cost-to-go function
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