In this work we present an overview of a Deep Reinforcement Learning method using Q-learning that recently brought the state-of-the-art results in the Atari Emulator Environment. This method is called DQN algorithm that combines successfully a convolutional neural network with Q-learning. Successively it was improved with Double Q-learning and other valid techniques. Attracted by this hot research area we replicated the DQN algorithm and run it in the CartPole environment, a famous toy control challenge that allowed us to deal with the main issues related to the implementation of DQN. In this paper we reported the results that we obtained, our consideration on them and some personal thoughts about the Deep Reinforcement Learning field.
https://docs.google.com/presentation/d/1DylaW-f55jYYyvB7s36DhKgBVZDhve7be8MmX9abGA0/edit?usp=sharing
See the following repository: https://github.com/EduBic/DQN-in-CartPole
- Playing Atari with Deep Reinforcement Learning [1]: https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf
- Human-level control through deep reinforcement learning [2]: https://www.nature.com/articles/nature14236
- Double Q Learning [3]: https://arxiv.org/abs/1509.06461
- Deep Reinforcement Learning with Double Q-learning [4]: https://arxiv.org/abs/1509.06461