Reinforcement Learning and Deep Learning Lab

List of Practical

1. Setting up the Spyder IDE Environment and Executing a Python Program
2. Installing Keras, Tensorflow and Pytorch libraries and making use of them 
3. Implement Q-learning with pure Python to play a game
• Environment set up and intro to OpenAI Gym
• Write Q-learning algorithm and train agent to play game
• Watch trained agent play game 
4. Implement deep Q-network with PyTorch 
5. Python implementation of the iterative policy evaluation and update.
6. Chatbot using bi-directional LSTMs
7. Image classification on MNIST dataset (CNN model with fully connected layer)
8. Train a sentiment analysis model on IMDB dataset, use RNN layers with LSTM/GRU
9. Applying the Deep Learning Models in the field of Natural Language Processing
10. Applying the Convolution Neural Network on computer vision problems