強化学習２１　Colaboratory+Pendulum+ChainerRL+A2C

強化学習２０まで終了していることが前提です。

chainerRLをgithubからcloneすると、examplesの中に、pendulumをいろいろなアルゴリズムで強化学習するのがありました。
とりあえず、アルファベット順にやってみます。

まずは、A2Cです。
Advantage　Actor-Critic
の略です。
Actor：　やることを決定
Critic：　状態評価
の両方を同時に最適化するアルゴリズムです。
複数スレッドでagentを並列に実行して学習することができます。
unityのmlagentはこれでしょうか？

colaboratory notebookです。
学習時間は１時間位でした。
CPUモードのほうが速い。

#Google drive mount

```
import google.colab.drive
google.colab.drive.mount('gdrive')
!ln -s gdrive/My\ Drive mydrive
```
#program install

```
!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1
!pip install pyvirtualdisplay > /dev/null 2>&1
!pip -q install JSAnimation
!pip -q install chainerrl
```
#Main program
An example of training A2C against OpenAI Gym Envs.

This script is an example of training a A2C agent against OpenAI Gym envs.
Both discrete and continuous action spaces are supported.

## modules import

```python

from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import *  # NOQA
from future import standard_library
standard_library.install_aliases()  # NOQA
import argparse
import functools

import chainer
from chainer import functions as F
import gym
import numpy as np
import os
import sys

import chainerrl
from chainerrl.agents import a2c
from chainerrl import experiments
from chainerrl import links
from chainerrl import misc
from chainerrl.optimizers.nonbias_weight_decay import NonbiasWeightDecay
from chainerrl import policies
from chainerrl import v_function
```
## Class A2CFFSoftmax
An example of A2C feedforward softmax policy.

```python

class A2CFFSoftmax(chainer.ChainList, a2c.A2CModel):
    def __init__(self, ndim_obs, n_actions, hidden_sizes=(64, 64)):
        self.pi = policies.SoftmaxPolicy(
            model=links.MLP(ndim_obs, n_actions, hidden_sizes))
        self.v = links.MLP(ndim_obs, 1, hidden_sizes=hidden_sizes)
        super().__init__(self.pi, self.v)

    def pi_and_v(self, state):
        return self.pi(state), self.v(state)
```

##Class A2CFFMellowmax
An example of A2C feedforward mellowmax policy.

```python

class A2CFFMellowmax(chainer.ChainList, a2c.A2CModel):
    def __init__(self, ndim_obs, n_actions, hidden_sizes=(64, 64)):
        self.pi = policies.MellowmaxPolicy(
            model=links.MLP(ndim_obs, n_actions, hidden_sizes))
        self.v = links.MLP(ndim_obs, 1, hidden_sizes=hidden_sizes)
        super().__init__(self.pi, self.v)

    def pi_and_v(self, state):
        return self.pi(state), self.v(state)
```
##Class A2CGaussian
An example of A2C recurrent Gaussian policy.

```python

class A2CGaussian(chainer.ChainList, a2c.A2CModel):
    def __init__(self, obs_size, action_size):
        self.pi = policies.FCGaussianPolicyWithFixedCovariance(
            obs_size,
            action_size,
            np.log(np.e - 1),
            n_hidden_layers=2,
            n_hidden_channels=64,
            nonlinearity=F.tanh)
        self.v = v_function.FCVFunction(obs_size, n_hidden_layers=2,
                                        n_hidden_channels=64,
                                        nonlinearity=F.tanh)
        super().__init__(self.pi, self.v)

    def pi_and_v(self, state):
        return self.pi(state), self.v(state)
```
##Main

###args

```python

import logging

parser = argparse.ArgumentParser()
parser.add_argument('--env', type=str, default='Pendulum-v0')
parser.add_argument('--arch', type=str, default='Gaussian',choices=('FFSoftmax', 'FFMellowmax', 'Gaussian'))
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--outdir', type=str, default='mydrive/OpenAI/Pendulum/result-a2c')
parser.add_argument('--profile', action='store_true')
parser.add_argument('--steps', type=int, default=8 * 10 ** 7)
parser.add_argument('--update-steps', type=int, default=5)
parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument('--eval-interval', type=int, default=10 ** 5)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--rmsprop-epsilon', type=float, default=1e-5)
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--gamma', type=float, default=0.99)
parser.add_argument('--use-gae', action='store_true', default=False)
parser.add_argument('--tau', type=float, default=0.95)
parser.add_argument('--lr', type=float, default=7e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.INFO)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--max-grad-norm', type=float, default=0.5)
parser.add_argument('--alpha', type=float, default=0.99)
parser.add_argument('--gpu', '-g', type=int, default=-1)
parser.add_argument('--num-envs', type=int, default=1)
```

変更したいところは、

args =parser.parse_args(['--env','CartPole-v0'])

のようにする。

```python

args = parser.parse_args('')
logging.basicConfig(level=args.logger_level, stream=sys.stdout, format='')
```
Set a random seed used in ChainerRL.

If you use more than one processes, the results will be no longer
deterministic even with the same random seed.
 
```python

misc.set_random_seed(args.seed)

    # Set different random seeds for different subprocesses.
    # If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
    # If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].


process_seeds = np.arange(args.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2 ** 32
if not os.path.exists(args.outdir):
  os.makedirs(args.outdir)

###function

def make_env(process_idx, test):
    env = gym.make(args.env)
    # Use different random seeds for train and test envs
    process_seed = int(process_seeds[process_idx])
    env_seed = 2 ** 32 - 1 - process_seed if test else process_seed
    env.seed(env_seed)
    # Cast observations to float32 because our model uses float32
    env = chainerrl.wrappers.CastObservationToFloat32(env)
    if args.monitor and process_idx == 0:
        env = chainerrl.wrappers.Monitor(env, args.outdir)
    # Scale rewards observed by agents
    if not test:
        misc.env_modifiers.make_reward_filtered(
            env, lambda x: x * args.reward_scale_factor)
    if args.render and process_idx == 0 and not test:
        env = chainerrl.wrappers.Render(env)
    return env

def make_batch_env(test):
    return chainerrl.envs.MultiprocessVectorEnv(
        [functools.partial(make_env, idx, test)
            for idx, env in enumerate(range(args.num_envs))])
```

actionのタイプでモデルを選択します。


```python

sample_env = make_env(process_idx=0, test=False)
timestep_limit = sample_env.spec.tags.get(
    'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space

# Switch policy types accordingly to action space types
if args.arch == 'Gaussian':
    model = A2CGaussian(obs_space.low.size, action_space.low.size)
elif args.arch == 'FFSoftmax':
    model = A2CFFSoftmax(obs_space.low.size, action_space.n)
elif args.arch == 'FFMellowmax':
    model = A2CFFMellowmax(obs_space.low.size, action_space.n)

optimizer = chainer.optimizers.RMSprop(args.lr,
                                        eps=args.rmsprop_epsilon,
                                        alpha=args.alpha)
optimizer.setup(model)
optimizer.add_hook(chainer.optimizer.GradientClipping(args.max_grad_norm))
if args.weight_decay > 0:
    optimizer.add_hook(NonbiasWeightDecay(args.weight_decay))

agent = a2c.A2C(model, optimizer, gamma=args.gamma,
                gpu=args.gpu,
                num_processes=args.num_envs,
                update_steps=args.update_steps,
                use_gae=args.use_gae,
                tau=args.tau)
if args.load:
    agent.load(args.load)

experiments.train_agent_batch_with_evaluation(
    agent=agent,
    env=make_batch_env(test=False),
    eval_env=make_batch_env(test=True),
    steps=args.steps,
    log_interval=args.log_interval,
    eval_n_steps=None,
    eval_n_episodes=args.eval_n_runs,
    eval_interval=args.eval_interval,
    outdir=args.outdir,
)
agent.save(args.outdir+'/agent')
```

```python

import pandas as pd
import glob
import os
score_files = glob.glob(args.outdir+'/scores.txt')
score_files.sort(key=os.path.getmtime)
score_file = score_files[-1]
df = pd.read_csv(score_file, delimiter='\t' )
df
```

```python

df.plot(x='steps',y='average_actor')
```

```python

from pyvirtualdisplay import Display
display = Display(visible=0, size=(1024, 768))
display.start()
```

```python

from JSAnimation.IPython_display import display_animation
from matplotlib import animation
import matplotlib.pyplot as plt
%matplotlib inline
```

```python

frames = []
env = gym.make(args.env)
process_seeds = np.arange(args.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2 ** 32
env_seed = int(process_seeds[0])
env.seed(env_seed)
env = chainerrl.wrappers.CastObservationToFloat32(env)
env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)

envw = gym.wrappers.Monitor(env, args.outdir, force=True)

for i in range(3):
    obs = envw.reset()
    done = False
    R = 0
    t = 0
    while not done and t < 200:
        frames.append(envw.render(mode = 'rgb_array'))
        action = agent.act(obs)
        obs, r, done, _ = envw.step(action)
        R += r
        t += 1
    print('test episode:', i, 'R:', R)
    agent.stop_episode()
#envw.render()
envw.close()

from IPython.display import HTML
plt.figure(figsize=(frames[0].shape[1]/72.0, frames[0].shape[0]/72.0),dpi=72)
patch = plt.imshow(frames[0])
plt.axis('off') 
def animate(i):
    patch.set_data(frames[i])
anim = animation.FuncAnimation(plt.gcf(), animate, frames=len(frames),interval=50)
anim.save(args.outdir+'/test.mp4')
HTML(anim.to_jshtml())
```