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AI 딥러닝/강화학습

Tensorflow2로 만든 DDPG 코드: BipedalWalker-v3

by 세인트워터멜론 2021. 7. 9.

OpenAI Gym에서 제공하는 BipedalWalker-v3 환경을 대상으로 DDPG 알고리즘을 Tensorflow2 코드로 구현하였다.

 

 

BipedalWalker-v3는 DDPG로 학습하기에는 난해한 문제로 알려져 있다. 하이퍼파라미터에 매우 민감하기 때문이다.

학습결과는 다음과 같다. 500회의 에피소드로 학습한 결과다. 추세를 볼 때 그 이상 학습한다면 더 좋은 결과를 얻을 수도 있을 것 같다.

 

 

학습하기 전 워커의 움직임은 다음과 같다.

 

 

아래는 학습 중간에 얻은 결과다.

 

 

다음은 학습이 끝난 후 워커의 움직임이다.

 

 

DDPG 코드는 액터-크리틱 신경망을 구현하고 학습시키기 위한 ddpg_learn.py, 이를 실행시키기 위한 ddpg_main.py, 학습을 마친 신경망 파라미터를 읽어와 에이전트를 구동하기 위한 ddpg_load_play.py, 그리고 리플레이 버퍼를 구현한 replaybuffer.py로 구성되어 있다. 전체 코드 구조는 다음과 같다.

 

 

다음은 Tensorflow2 코드다.

 

ddpg_learn.py

 

# DDPG learn (tf2 subclassing version: using chain rule to train Actor)
# BipedalWalker-v3
# coded by St.Watermelon

import numpy as np
import matplotlib.pyplot as plt

from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, concatenate
from tensorflow.keras.optimizers import Adam
import tensorflow as tf

from replaybuffer import ReplayBuffer

## actor network
class Actor(Model):

    def __init__(self, action_dim, action_bound):
        super(Actor, self).__init__()

        self.action_bound = action_bound

        self.h1 = Dense(400, activation='relu')
        self.h2 = Dense(300, activation='relu',
                        bias_initializer=
                        tf.keras.initializers.random_uniform(
                            minval=-0.003, maxval=0.003))
        self.action = Dense(action_dim, activation='tanh')


    def call(self, state):
        x = self.h1(state)
        x = self.h2(x)
        a = self.action(x)

        return a


## critic network
class Critic(Model):

    def __init__(self):
        super(Critic, self).__init__()

        self.h1 = Dense(400, activation='relu',
                        kernel_regularizer=tf.keras.regularizers.L2(0.01))
        self.h2 = Dense(300, activation='relu',
                        bias_initializer=
                        tf.keras.initializers.random_uniform(
                            minval=-0.003, maxval=0.003),
                        kernel_regularizer=tf.keras.regularizers.L2(0.01))
        self.q = Dense(1, activation='linear')


    def call(self, state_action):
        state = state_action[0]
        action = state_action[1]
        xa = concatenate([state, action], axis=-1)
        x = self.h1(xa)
        x = self.h2(x)
        q = self.q(x)
        return q


## agent
class DDPGagent(object):

    def __init__(self, env):

        # hyperparameters
        self.GAMMA = 0.99
        self.BATCH_SIZE = 64
        self.BUFFER_SIZE = 1e6
        self.ACTOR_LEARNING_RATE = 0.001
        self.CRITIC_LEARNING_RATE = 0.01
        self.TAU = 0.1

        self.env = env
        # get state dimension
        self.state_dim = env.observation_space.shape[0]
        # get action dimension
        self.action_dim = env.action_space.shape[0]
        # get action bound
        self.action_bound = env.action_space.high[0]

        # create actor and critic networks
        self.actor = Actor(self.action_dim, self.action_bound)
        self.target_actor = Actor(self.action_dim, self.action_bound)

        self.critic = Critic()
        self.target_critic = Critic()

        self.actor.build(input_shape=(None, self.state_dim))
        self.target_actor.build(input_shape=(None, self.state_dim))

        state_in = Input((self.state_dim,))
        action_in = Input((self.action_dim,))
        self.critic([state_in, action_in])
        self.target_critic([state_in, action_in])

        self.actor.summary()
        self.critic.summary()

        # optimizer
        self.actor_opt = Adam(self.ACTOR_LEARNING_RATE)
        self.critic_opt = Adam(self.CRITIC_LEARNING_RATE)

        # initialize replay buffer
        self.buffer = ReplayBuffer(self.BUFFER_SIZE)

        # save the results
        self.save_epi_reward = []


    ## transfer actor weights to target actor with a aau
    def update_target_network(self, TAU):
        theta = self.actor.get_weights()
        target_theta = self.target_actor.get_weights()
        for i in range(len(theta)):
            target_theta[i] = TAU * theta[i] + (1 - TAU) * target_theta[i]
        self.target_actor.set_weights(target_theta)

        phi = self.critic.get_weights()
        target_phi = self.target_critic.get_weights()
        for i in range(len(phi)):
            target_phi[i] = TAU * phi[i] + (1 - TAU) * target_phi[i]
        self.target_critic.set_weights(target_phi)


    ## single gradient update on a single batch data
    def critic_learn(self, states, actions, td_targets):
        with tf.GradientTape() as tape:
            q = self.critic([states, actions], training=True)
            loss = tf.reduce_mean(tf.square(q-td_targets))

        grads = tape.gradient(loss, self.critic.trainable_variables)
        self.critic_opt.apply_gradients(zip(grads, self.critic.trainable_variables))


    ## train the actor network
    def actor_learn(self, states):
        with tf.GradientTape() as tape:
            actions = self.actor(states, training=True)
            critic_q = self.critic([states, actions])
            loss = -tf.reduce_mean(critic_q)

        grads = tape.gradient(loss, self.actor.trainable_variables)
        self.actor_opt.apply_gradients(zip(grads, self.actor.trainable_variables))


    ## computing TD target: y_k = r_k + gamma*Q(x_k+1, u_k+1)
    def td_target(self, rewards, q_values, dones):
        y_k = np.asarray(q_values)
        for i in range(q_values.shape[0]): # number of batch
            if dones[i]:
                y_k[i] = rewards[i]
            else:
                y_k[i] = rewards[i] + self.GAMMA * q_values[i]
        return y_k


    ## load actor weights
    def load_weights(self, path):
        self.actor.load_weights(path + 'walker_actor.h5')
        self.critic.load_weights(path + 'walker_critic.h5')


    ## train the agent
    def train(self, max_episode_num):

        # initial transfer model weights to target model network
        self.update_target_network(1.0)

        for ep in range(int(max_episode_num)):
            # reset episode
            time, episode_reward, done = 0, 0, False
            # reset the environment and observe the first state
            state = self.env.reset()

            while not done:
                # pick an action: shape = (4,)
                action = self.actor(tf.convert_to_tensor([state], dtype=tf.float32))
                action = action.numpy()[0]
                noise = np.random.randn(self.action_dim) * 0.1
                # clip continuous action to be within action_bound
                action = np.clip(action + noise, -self.action_bound, self.action_bound)
                # observe reward, new_state
                next_state, reward, done, _ = self.env.step(action)
                trained_reward = reward * 10.0

                self.buffer.add_buffer(state, action, trained_reward, next_state, done)

                if self.buffer.buffer_count() > 1000:  # start train after buffer has some amounts

                    # sample transitions from replay buffer
                    states, actions, rewards, next_states, dones = self.buffer.sample_batch(self.BATCH_SIZE)

                    # predict target Q-values
                    target_qs = self.target_critic([tf.convert_to_tensor(next_states, dtype=tf.float32),
                                                    self.target_actor(
                                                        tf.convert_to_tensor(next_states, dtype=tf.float32))])
                    # compute TD targets
                    y_i = self.td_target(rewards, target_qs.numpy(), dones)

                    # train critic using sampled batch
                    self.critic_learn(tf.convert_to_tensor(states, dtype=tf.float32),
                                      tf.convert_to_tensor(actions, dtype=tf.float32),
                                      tf.convert_to_tensor(y_i, dtype=tf.float32))

                    # train actor
                    self.actor_learn(tf.convert_to_tensor(states, dtype=tf.float32))
                    # update both target network
                    self.update_target_network(self.TAU)

                # update current state
                state = next_state
                episode_reward += reward
                time += 1

            # display rewards every episode
            print('Episode: ', ep+1, 'Time: ', time, 'Reward: ', episode_reward)

            self.save_epi_reward.append(episode_reward)


            # save weights every episode
            if ep % 10 == 0:
                self.actor.save_weights("./save_weights/walker_actor.h5")
                self.critic.save_weights("./save_weights/walker_critic.h5")

        np.savetxt('./save_weights/walker_epi_reward.txt', self.save_epi_reward)
        print(self.save_epi_reward)


    ## save them to file if done
    def plot_result(self):
        plt.plot(self.save_epi_reward)
        plt.show()

 

 

 

ddpg_main.py

 

# DDPG main (tf2 subclassing API version)
# coded by St.Watermelon

import gym
from ddpg_learn import DDPGagent

def main():

    max_episode_num = 500
    env = gym.make("BipedalWalker-v3")
    agent = DDPGagent(env)

    agent.train(max_episode_num)

    agent.plot_result()

if __name__=="__main__":
    main()

 

ddpg_load_play.py

 

# DDPG load and play (tf2 subclassing API version)
# coded by St.Watermelon

import gym
from ddpg_learn import DDPGagent
import tensorflow as tf

def main():

    env = gym.make("BipedalWalker-v3")
    agent = DDPGagent(env)

    agent.load_weights('./save_weights/')

    time = 0
    state = env.reset()

    while True:
        env.render()
        action = agent.actor(tf.convert_to_tensor([state], dtype=tf.float32)).numpy()[0]
        state, reward, done, _ = env.step(action)
        time += 1

        print('Time: ', time, 'Reward: ', reward)

        if done:
            break

    env.close()

if __name__=="__main__":
    main()

 

 

 

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