%tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')

!pip install 'gym[atari]'

import deepchem as dc
import numpy as np

class PongEnv(dc.rl.GymEnvironment):
  def __init__(self):
    super(PongEnv, self).__init__('Pong-v0')
    self._state_shape = (80, 80)
  
  @property
  def state(self):
    # Crop everything outside the play area, reduce the image size,
    # and convert it to black and white.
    cropped = np.array(self._state)[34:194, :, :]
    reduced = cropped[0:-1:2, 0:-1:2]
    grayscale = np.sum(reduced, axis=2)
    bw = np.zeros(grayscale.shape)
    bw[grayscale != 233] = 1
    return bw

  def __deepcopy__(self, memo):
    return PongEnv()

env = PongEnv()

"""
Explanation: Tutorial Part 18: Using Reinforcement Learning to Play Pong
This notebook demonstrates using reinforcement learning to train an agent to play Pong.
The first step is to create an Environment that implements this task.  Fortunately,
OpenAI Gym already provides an implementation of Pong (and many other tasks appropriate
for reinforcement learning).  DeepChem's GymEnvironment class provides an easy way to
use environments from OpenAI Gym.  We could just use it directly, but in this case we
subclass it and preprocess the screen image a little bit to make learning easier.
Colab
This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.

Setup
To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. To install gym you should also use pip install 'gym[atari]' (We need the extra modifier since we'll be using an atari game). We'll add this command onto our usual Colab installation commands for you
End of explanation
"""


import tensorflow as tf
from tensorflow.keras.layers import Input, Concatenate, Conv2D, Dense, Flatten, GRU, Reshape

class PongPolicy(dc.rl.Policy):
    def __init__(self):
        super(PongPolicy, self).__init__(['action_prob', 'value', 'rnn_state'], [np.zeros(16)])

    def create_model(self, **kwargs):
        state = Input(shape=(80, 80))
        rnn_state = Input(shape=(16,))
        conv1 = Conv2D(16, kernel_size=8, strides=4, activation=tf.nn.relu)(Reshape((80, 80, 1))(state))
        conv2 = Conv2D(32, kernel_size=4, strides=2, activation=tf.nn.relu)(conv1)
        dense = Dense(256, activation=tf.nn.relu)(Flatten()(conv2))
        gru, rnn_final_state = GRU(16, return_state=True, return_sequences=True)(
            Reshape((-1, 256))(dense), initial_state=rnn_state)
        concat = Concatenate()([dense, Reshape((16,))(gru)])
        action_prob = Dense(env.n_actions, activation=tf.nn.softmax)(concat)
        value = Dense(1)(concat)
        return tf.keras.Model(inputs=[state, rnn_state], outputs=[action_prob, value, rnn_final_state])

policy = PongPolicy()

"""
Explanation: Next we create a network to implement the policy.  We begin with two convolutional layers to process
the image.  That is followed by a dense (fully connected) layer to provide plenty of capacity for game
logic.  We also add a small Gated Recurrent Unit.  That gives the network a little bit of memory, so
it can keep track of which way the ball is moving.
We concatenate the dense and GRU outputs together, and use them as inputs to two final layers that serve as the
network's outputs.  One computes the action probabilities, and the other computes an estimate of the
state value function.
We also provide an input for the initial state of the GRU, and returned its final state at the end.  This is required by the learning algorithm
End of explanation
"""


from deepchem.models.optimizers import Adam
a3c = dc.rl.A3C(env, policy, model_dir='model', optimizer=Adam(learning_rate=0.0002))

"""
Explanation: We will optimize the policy using the Asynchronous Advantage Actor Critic (A3C) algorithm.  There are lots of hyperparameters we could specify at this point, but the default values for most of them work well on this problem.  The only one we need to customize is the learning rate.
End of explanation
"""


# Change this to train as many steps as you have patience for.
a3c.fit(1000)

"""
Explanation: Optimize for as long as you have patience to.  By 1 million steps you should see clear signs of learning.  Around 3 million steps it should start to occasionally beat the game's built in AI.  By 7 million steps it should be winning almost every time.  Running on my laptop, training takes about 20 minutes for every million steps.
End of explanation
"""


# This code doesn't work well on Colab
env.reset()
while not env.terminated:
    env.env.render()
    env.step(a3c.select_action(env.state))

"""
Explanation: Let's watch it play and see how it does!
End of explanation
"""


