Here I use, CEM method to solve the environment. In this method, a small noise is added to weights to the neural network instead on the actions taken by the agent. It is an off policy reinforcement learning method. It uses a tanh activation function in the final layer - it can be used for continouse state action space.

• Instantiate a weight matrix. For every episode, a small amount of noise is added to the weight matrix and rewards are evaluated. Its like genetic evolution method -
• For every episode, you take a set of weights(by adding noise everytime to the weight matrix) and calculate the rewards obtained using those weights.
• Then you sort those rewards and only take the top 10/ whatever the elite number and get the best weights corresponding to those rewards.
• In the end, you take the mean of those top weights and then calculate the reward with that mean weight.
• Repeat step 2-4 for number of episodes, with the mean weight and add noice to it to get correct set of weights.

Projects solved:

• 1 - MountainCar_Continous - For info on the environment - https://github.com/openai/gym/wiki/MountainCarContinuous-v0