Metadata-Version: 2.1
Name: DDPG-TF
Version: 2.0.3
Summary: DDPG implimentaion in Tensorflow-2.0 
Home-page: https://github.com/Dekki-Aero/DDPG
Author: Dekki
Author-email: dekkiaero@gmail.com
License: UNKNOWN
Description: DDPG
        ============
        
        - Implimenting DDPG Algorithm in Tensorflow-2.0
        - Tested on Open-AI Pendulum-v0 and Continous mountain car gym environments.
        - DDPG - algorthim : https://arxiv.org/abs/1509.02971
        
        Install :
        ------------------
        - pip install DDPG-TF
        
        
        python code:
        ------------------
        ```python
        import gym
        
        from ddpg import DDPG
        
        env = gym.make('Pendulum-v0')
        
        ddpg = DDPG(
                         env , # Gym environment with continous action space
                         actor(None), # Tensorflow/keras model
                         critic (None), # Tensorflow/keras model
                         buffer (None), # pre-recorded buffer
                         action_bound_range=1,
                         max_buffer_size =10000, # maximum transitions to be stored in buffer
                         batch_size =64, # batch size for training actor and critic networks
                         max_time_steps = 1000 ,# no of time steps per epoch
                         tow = 0.001, # for soft target update
                         discount_factor  = 0.99,
                         explore_time = 1000, # time steps for random actions for exploration
                         actor_learning_rate = 0.0001,
                         critic_learning_rate = 0.001
                         dtype = 'float32',
                         n_episodes = 1000 ,# no of episodes to run
                         reward_plot = True ,# (bool)  to plot reward progress per episode
                         model_save = 1) # epochs to save models and buffer
        
        ddpg.train()
        ```
        
        ------------
        
        
        ## Results :
        
        - On pendulum problem explored for 5 episodes
        
        
        [![Reward plot of Pendulum problem](https://github.com/Dekki-Aero/DDPG/blob/master/DDPG-Pendulum_Performance.png "Reward plot of Pendulum problem")](http://https://github.com/Dekki-Aero/DDPG/blob/master/DDPG-Pendulum_Performance.png "Reward plot of Pendulum problem")
        
        - On Continous mountain car problem explored for 100 episodes
        
        
        [![Reward plot of CountinousMountain car](https://github.com/Dekki-Aero/DDPG/blob/master/mountain_car_continous.png "Reward plot of CountinousMountain car")](http://https://github.com/Dekki-Aero/DDPG/blob/master/mountain_car_continous.png "Reward plot of CountinousMountain car")
        
Keywords: Deep Determnistic policy gradient,Actor Critic,Reinforcement Learning,DDPG
Platform: UNKNOWN
Description-Content-Type: text/markdown
