!pip install --pre deepchem
import deepchem
deepchem.__version__

!pip install 'gym[atari]'

"""
Explanation: Using Reinforcement Learning to Play Pong
This tutorial demonstrates using reinforcement learning to train an agent to play Pong.  This task isn't directly related to chemistry, but video games make an excellent demonstration of reinforcement learning techniques.

Colab
This tutorial and the rest in this sequence can be done in Google Colab (although the visualization at the end doesn't work correctly on Colab, so you might prefer to run this tutorial locally). If you'd like to open this notebook in colab, you can use the following link.
End of explanation
"""


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: Reinforcement Learning
Reinforcement learning involves an agent that interacts with an environment.  In this case, the environment is the video game and the agent is the player.  By trial and error, the agent learns a policy that it follows to perform some task (winning the game).  As it plays, it receives rewards that give it feedback on how well it is doing.  In this case, it receives a positive reward every time it scores a point and a negative reward every time the other player scores a point.
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.
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, time_major=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 model to implement our policy.  This model receives the current state of the environment (the pixels being displayed on the screen at this moment) as its input.  Given that input, it decides what action to perform.  In Pong there are three possible actions at any moment: move the paddle up, move it down, or leave it where it is.  The policy model produces a probability distribution over these actions.  It also produces a value output, which is interpreted as an estimate of how good the current state is.  This turns out to be important for efficient learning.
The model begins 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 (GRU).  That gives the network a little bit of memory, so it can keep track of which way the ball is moving.  Just from the screen image, you cannot tell whether the ball is moving to the left or to the right, so having memory is important.
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 return its final state at the end.  This is required by the learning algorithm.
End of explanation
"""


from deepchem.models.optimizers import Adam
a2c = dc.rl.A2C(env, policy, model_dir='model', optimizer=Adam(learning_rate=0.0002))

"""
Explanation: We will optimize the policy using the Advantage Actor Critic (A2C) 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.
a2c.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(a2c.select_action(env.state))

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


