Disclaimer - This is a repository containing the assignments of 10703 - Deep Reinforcement Learning & Control for Spring'18. If you are currently taking the course, please do NOT refer to this repo. Any kind of code copying or referencing will be treated as plagiarism and results in serious disciplinary action at CMU.

Implementations of RL algorithms-

  • DQN
  • Advantage Actor-Critic
  • Imitation Learning
  • REINFORCE
  • Dueling DQN
CartPole Mountain Car LunarLander
CartPole MountainCar LunarLander

Requirements-

  • TensorFlow
  • Keras
  • Gym Box2D envs

To run DQN and Dueling DQN -
python DQN_Implementation.py with the following arguments-

Argument Description
--env=ENVIRONMENT_NAME CartPole-v0, MountainCar-v0, LunarLander-v2
--render=1 OR 0 variable to enable render(1) or not(0)
--train=1 OR 0 variable to train(1) the model or not(0)
--type=MODEL_TYPE DQN,Dueling
--save_folder=FOLDER_DIR folder directory to save videos (Optional). Videos are not saved if nothing is given
--model_file=FILE_DIR File directory of saved model(Optional). Nothing is done if not given

HyperParameters have been sectioned for easy alteration. You should be able to locate them easily by just searching 'Hyper'.

To run -

  • Advantage-Actor Critic - python a2c.py
  • REINFORCE - python reinforce.py --render
  • Imitation - python imitation --render (Weights are only for LunarLander-v2)

Rl_algos

Reinforcement Learning Algorithms

Rl_algos Info

⭐ Stars 14
🔗 Source Code github.com
🕒 Last Update 2 years ago
🕒 Created 3 years ago
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😎 Author satyenrajpal