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from __future__ import print_function

import argparse
import gym
from itertools import count
import numpy as onp

import mxnet as mx
from mxnet import gluon
from mxnet.gluon import nn
from mxnet import autograd, npx


parser = argparse.ArgumentParser(description='MXNet actor-critic example')
parser.add_argument('--gamma', type=float, default=0.99, metavar='G',
                    help='discount factor (default: 0.99)')
parser.add_argument('--seed', type=int, default=543, metavar='N',
                    help='random seed (default: 1)')
parser.add_argument('--render', action='store_true',
                    help='render the environment')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='interval between training status logs (default: 10)')
args = parser.parse_args()


env = gym.make('CartPole-v0')
env.seed(args.seed)


class Policy(gluon.Block):
    def __init__(self, **kwargs):
        super(Policy, self).__init__(**kwargs)
        self.dense = nn.Dense(16, in_units=4, activation='relu')
        self.action_pred = nn.Dense(2, in_units=16)
        self.value_pred = nn.Dense(1, in_units=16)

    def forward(self, x):
        x = self.dense(x)
        probs = self.action_pred(x)
        values = self.value_pred(x)
        return npx.softmax(probs), values

net = Policy()
net.initialize(mx.init.Uniform(0.02))
trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': 3e-2})
loss = gluon.loss.L1Loss()

running_reward = 10
for epoch in count(1):
    state = env.reset()
    rewards = []
    values = []
    heads = []
    actions = []
    with autograd.record():
        # Sample a sequence of actions
        for t in range(10000):
            state = mx.nd.array(onp.expand_dims(state, 0))
            prob, value = net(state.as_np_ndarray())
            action, logp = mx.nd.sample_multinomial(prob.as_nd_ndarray(), get_prob=True)
            state, reward, done, _ = env.step(action.asnumpy()[0])
            if args.render:
                env.render()
            rewards.append(reward)
            values.append(value.as_np_ndarray())
            actions.append(action.asnumpy()[0])
            heads.append(logp)
            if done:
                break

        # reverse accumulate and normalize rewards
        running_reward = running_reward * 0.99 + t * 0.01
        R = 0
        for i in range(len(rewards)-1, -1, -1):
            R = rewards[i] + args.gamma * R
            rewards[i] = R
        rewards = onp.array(rewards)
        rewards -= rewards.mean()
        rewards /= rewards.std() + onp.finfo(rewards.dtype).eps

        # compute loss and gradient
        L = sum([loss(value, mx.np.array([r])) for r, value in zip(rewards, values)])
        final_nodes = [L]
        for logp, r, v in zip(heads, rewards, values):
            reward = r - v.asnumpy()[0,0]
            # Here we differentiate the stochastic graph, corresponds to the
            # first term of equation (6) in https://arxiv.org/pdf/1506.05254.pdf
            # Optimizer minimizes the loss but we want to maximizing the reward,
            # so use we use -reward here.
            final_nodes.append(logp*(-reward))
        autograd.backward(final_nodes)

    trainer.step(t)

    if epoch % args.log_interval == 0:
        print('Episode {}\tLast length: {:5d}\tAverage length: {:.2f}'.format(
            epoch, t, running_reward))
    if running_reward > 200:
        print("Solved! Running reward is now {} and "
              "the last episode runs to {} time steps!".format(running_reward, t))
        break
