• Implement DDPG ( Deep Deterministic Policy Gradient)

• solve the problem that if epochs are over 200, then the action is converged in wrong direction.
• more games have to be tested.
• parser

1. Save error and notation fixed
2. argparser added

1. replaybuffer.py's sampling method is changed.
2. new test result added.
3. pendulum-v0 is now testing.

• As epochs over 200, all(train and test) models are diverged.
  • i tried to adjust batch size, learning-rate, activation function, model size, noise size but it is not cleared.

• it doesn't converged at all.
  • i tried almost same model maded by another people, it looks same i guess , but it looks converged. but my model didn't converged.

• i changed the training rate in Critic model at 0.001 to 0.0001(i have tried some points.)
  • it shows that model can be trained well by adjusting the learning rate. i gain the idea from TRPO and PPO that the change of model of parameters is handled carefully.

python main.py

• If you want to change hyper-parameters, you can check "python main.py --help"

Options:

• '--epochs', type=int, default=100, help='number of epochs, (default: 100)'
• '--e', type=str, default='MountainCarContinuous-v0', help='environment name, (default: MountainCarContinuous-v0)' #- '--d', type=bool, default=False, help='train and test alternately. (default : False)'
• '--t', type=bool, default=True, help="True if training, False if test. (default: True)"
• '--r', type=bool, default=False, help='rendering the game environment. (default : False)'
• '--b', type=int, default=128, help='train batch size. (default : 128)'
• '--v', type=bool, default=False, help='verbose mode. (default : False)' #- '--n', type=bool, default=True, help='reward normalization. (default : True)'
• '--sp', type=int, default=True, help='save point. epochs // sp. (default : 100)'