RL_Algorithms

A collection of RL algorithms within a modular code structure for easy additions of new algorithms.

Currently Implemented

The following algorithms have been implemented and have solved the OpenAI gym CartPole environments. The reward plots can be seen in ./plots.

• DDQN (Double Deep Q-Network) Solved at episode 516
  • model = DDQN
  • agent = DDQNAgent
  • trainer = DDQNTrainer
  • hidden = [128,128]
  • buffer_size = 1000
• A2C (Advantage Actor-Critic) Solved at episode 550
  • model = ActorCritic
  • agent = A2CAgent
  • trainer = A2CTrainer
  • hidden = [128]
  • buffer_size = 300

To Run

To train a model, change hyperparameters within the ./utils.py file and run ./main.py.