Mountain-Car

Q Learning and DQN for mountain car

================================= FILE DESCRIPTIONS

1)bipedal.py - contains code for BipedalWalker-v2 using NN 2)bipedal_test.py - contains model tester code for BipedalWalker-v2 3)BipedalWalker_model2.h5 - contains trained model file for BipedalWalker-v2

4)mountain_car_dqn.py - contains code for mountainCar-v0 using NN 5)mountain_car_q.py - contains code for mountainCar-v0 using Q Table 6)mountain_car.h5 - contains trained model file for mountainCar-v0 7)mountain_car_test.py - contains tester code for mountainCar-v0

8)Report.pdf - Report File 9)mean_reward.npy - contains output of mean_rewards obtained for every 100 episodes from BipedalWalker-v2 (open using numpy) 10)reward_history.npy - contains all rewards per episode from BipedalWalker-v2 (open using numpy)

================================= Libraries Used

1. Gym
2. Numpy
3. matplotlib
4. Keras
5. Pickle
6. random
7. time

================================= RUN INSTRUCTION

1. Make sure directory structure is maintained
2. Make sure all the libraries are installed
3. RUN mountain_car_q.py for MountainCar-v0 using Q Table (OPTIONAL)
4. RUN mountain_car_dqn/final_dqn.py for MountainCar-v0 using DQN (PROBLEM 1)
5. RUN mountain_car_test.py for implementation of dqn trained MountainCar-v0 model (for 400 iterations, using 128 batch size)
6. RUN bipedal.py for BipedalWalker-v2 using DQN (PROBLEM 2)
7. RUN bipedal_test.py for implementation of trained BipedalWalker-v2 model (for 1000 iterations, using 16 batch size)