Cartpole_ActorCritic.py

# coding: utf-8

# In[2]:


# %load cartpole_a2c.py
import sys
import gym
import pylab
import numpy as np
from keras.layers import Dense
from keras.models import Sequential
from keras.optimizers import Adam

EPISODES = 1000


# A2C(Advantage Actor-Critic) agent for the Cartpole
class A2CAgent:
    def __init__(self, state_size, action_size):
        # if you want to see Cartpole learning, then change to True
        self.render = False
        self.load_model = False
        # get size of state and action
        self.state_size = state_size
        self.action_size = action_size
        self.value_size = 1

        # These are hyper parameters for the Policy Gradient
        self.discount_factor = 0.99
        self.actor_lr = 0.001
        self.critic_lr = 0.005

        # create model for policy network
        self.actor = self.build_actor()
        self.critic = self.build_critic()

        if self.load_model:
            self.actor.load_weights("./save_model/cartpole_actor.h5")
            self.critic.load_weights("./save_model/cartpole_critic.h5")

    # approximate policy and value using Neural Network
    # actor: state is input and probability of each action is output of model
    def build_actor(self):
        actor = Sequential()
        actor.add(Dense(24, input_dim=self.state_size, activation='relu',
                        kernel_initializer='he_uniform'))
        actor.add(Dense(self.action_size, activation='softmax',
                        kernel_initializer='he_uniform'))
        actor.summary()
        # See note regarding crossentropy in cartpole_reinforce.py
        actor.compile(loss='categorical_crossentropy',
                      optimizer=Adam(lr=self.actor_lr))
        return actor

    # critic: state is input and value of state is output of model
    def build_critic(self):
        critic = Sequential()
        critic.add(Dense(24, input_dim=self.state_size, activation='relu',
                         kernel_initializer='he_uniform'))
        critic.add(Dense(self.value_size, activation='linear',
                         kernel_initializer='he_uniform'))
        critic.summary()
        critic.compile(loss="mse", optimizer=Adam(lr=self.critic_lr))
        return critic

    # using the output of policy network, pick action stochastically
    def get_action(self, state):
        policy = self.actor.predict(state, batch_size=1).flatten()
        return np.random.choice(self.action_size, 1, p=policy)[0]

    # update policy network every episode
    def train_model(self, state, action, reward, next_state, done):
        target = np.zeros((1, self.value_size))
        advantages = np.zeros((1, self.action_size))

        value = self.critic.predict(state)[0]
        next_value = self.critic.predict(next_state)[0]

        if done:
            advantages[0][action] = reward - value
            target[0][0] = reward
        else:
            advantages[0][action] = reward + self.discount_factor * (next_value) - value
            target[0][0] = reward + self.discount_factor * next_value

        self.actor.fit(state, advantages, epochs=1, verbose=0)
        self.critic.fit(state, target, epochs=1, verbose=0)


if __name__ == "__main__":
    # In case of CartPole-v1, maximum length of episode is 500
    env = gym.make('CartPole-v1')
    # get size of state and action from environment
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n

    # make A2C agent
    agent = A2CAgent(state_size, action_size)

    scores, episodes = [], []

    for e in range(EPISODES):
        done = False
        score = 0
        state = env.reset()
        state = np.reshape(state, [1, state_size])

        while not done:
            if agent.render:
                env.render()

            action = agent.get_action(state)
            next_state, reward, done, info = env.step(action)
            next_state = np.reshape(next_state, [1, state_size])
            # if an action make the episode end, then gives penalty of -100
            reward = reward if not done or score == 499 else -100

            agent.train_model(state, action, reward, next_state, done)

            score += reward
            state = next_state

            if done:
                # every episode, plot the play time
                score = score if score == 500.0 else score + 100
                scores.append(score)
                episodes.append(e)
                pylab.plot(episodes, scores, 'b')
                pylab.savefig("./save_graph/cartpole_a2c.png")
                print("episode:", e, "  score:", score)

                # if the mean of scores of last 10 episode is bigger than 490
                # stop training
                if np.mean(scores[-min(10, len(scores)):]) > 490:
                    agent.actor.save_weights("./save_model/cartpole_actor.h5")
                    agent.critic.save_weights("./save_model/cartpole_critic.h5")
                    sys.exit()

        # save the model
        if e % 50 == 0:
            agent.actor.save_weights("./save_model/cartpole_actor.h5")
            agent.critic.save_weights("./save_model/cartpole_critic.h5")


# In[6]:


env = gym.make('CartPole-v1')
# get size of state and action from environment
state_size = env.observation_space.shape[0]
action_size = env.action_space.n

# make A2C agent
agent = A2CAgent(state_size, action_size)
agent.actor.load_weights("./save_model/cartpole_actor.h5")
agent.critic.load_weights("./save_model/cartpole_critic.h5")
agent.render = True

done = False
score = 0
state = env.reset()
state = np.reshape(state, [1, state_size])

while not done:
    if agent.render:
        env.render()

    action = agent.get_action(state)
    next_state, reward, done, info = env.step(action)
    next_state = np.reshape(next_state, [1, state_size])
            # if an action make the episode end, then gives penalty of -100
    reward = reward if not done or score == 499 else -100

    agent.train_model(state, action, reward, next_state, done)

    score += reward
    state = next_state

print(score)