Deep Reinforcement Learning

In this project I have implemented and tested neural network models for solving reinforcement learning problems.

Goals

Create and tune a deep reinforcement model in order to control the CartPole environment. Tune the exploration profile (either using eps-greedy or softmax) to improve the learning curve. Tune the model hyperparameters or tweak the reward function in order to speed-up learning convergence (i.e., reach the same accuracy with fewer training episodes).
Create and tune a deep reinforcement model in order to control the CartPole environment using directly the screen pixels, rather than the compact state representation (cart position, cart velocity, pole angle, pole angular velocity).
Train a deep RL agent on a different Gym environment.

A detailed report is present in the nn_homework_03.pdf file

Name		Name	Last commit message	Last commit date
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README.md		README.md
nn_homework_03.pdf		nn_homework_03.pdf
nndl_2020__homework_3_part_1.ipynb		nndl_2020__homework_3_part_1.ipynb
nndl_2020__homework_3_part_2_v1.ipynb		nndl_2020__homework_3_part_2_v1.ipynb
nndl_2020__homework_3_part_2_v2.ipynb		nndl_2020__homework_3_part_2_v2.ipynb
nndl_2020__homework_3_part_2_v3.ipynb		nndl_2020__homework_3_part_2_v3.ipynb
nndl_2020__homework_3_part_2_v4.ipynb		nndl_2020__homework_3_part_2_v4.ipynb
nndl_2020__homework_3_part_3.ipynb		nndl_2020__homework_3_part_3.ipynb