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vpg_quadball.py
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vpg_quadball.py
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import envi
import numpy as np
from collections import deque
import tensorflow as tf
from tensorflow.keras import losses as loss_fn
import tensorflow.keras.backend as keras_backend
class VanillaPolicyGradient(object):
def __init__(self, env):
self.input_shape = [env.observation_dimensions]
self.input_size = self.input_shape[0]
self.output_dim = env.action_space
self.hidden_dims = [64, 32]
self.losses = []
self.GAMMA = 0.9
self.LEARNING_RATE = 0.01
self.build_neural_net()
def build_neural_net(self):
input_layer = tf.keras.layers.Input(shape = self.input_shape,name="input")
advantage = tf.keras.layers.Input(shape=[1],name="advantage")
model = input_layer
for dim in self.hidden_dims:
# model = tf.keras.layers.Dense(dim,activation='tanh')(model)
model = tf.keras.layers.Dense(dim,activation='relu')(model)
output_layer = tf.keras.layers.Dense(self.output_dim,activation='softmax')(model)
# def vpg_loss(y_true, y_pred):
# likelihood = y_true * (y_true - y_pred) + (1 - y_true) * (y_true + y_pred)
# log_likelihood = keras_backend.log(likelihood)
# loss = keras_backend.mean(log_likelihood * advantage, keepdims=True)
# return loss
self.model_train = tf.keras.Model(inputs=[input_layer,advantage],outputs=output_layer)
self.model_train.compile(loss=loss_fn.msle,optimizer=tf.keras.optimizers.Adam(lr=self.LEARNING_RATE))
# self.model_train.compile(loss=loss_fn.kullback_leibler_divergence,optimizer=tf.keras.optimizers.Adam(lr=self.LEARNING_RATE))
# self.model_train.compile(loss=loss_fn.categorical_crossentropy,optimizer=tf.keras.optimizers.Adam(lr=self.LEARNING_RATE))
self.model_predict = tf.keras.Model(inputs=[input_layer],outputs=output_layer)
def get_discounted_rewards(self,reward_lst):
prev_val = 0
out = []
for val in reward_lst:
# print(val)
new_val = val + prev_val * self.GAMMA
# print(new_val)
out.append(new_val)
prev_val = new_val
# return np.array(out)
# print(np.array(out))
return np.array(out[::-1])
def fit(self,state_buffer,action_buffer,reward_buffer):
# print(action_buffer)
# print(state_buffer)
discounted_rewards = reward_buffer
# discounted_rewards -= keras_backend.mean(discounted_rewards)
discounted_rewards -= np.mean(discounted_rewards)
# print(discounted_rewards)
discounted_rewards /= np.std(discounted_rewards)
# print(discounted_rewards)
actions_train = np.zeros([len(action_buffer), self.output_dim])
actions_train[np.arange(len(action_buffer)), action_buffer] = 1
loss = self.model_train.train_on_batch([state_buffer, discounted_rewards], actions_train)
# print(loss)
self.losses.append(loss)
return loss
def get_action(self,state):
action_prob = np.squeeze(self.model_predict.predict(state))
# print(self.model_predict.predict(state))
# print(action_prob)
return np.random.choice(range(self.output_dim),p=action_prob)
def save_network_weights(self, save_location):
self.model_train.save_weights(save_location+'/training_model_weights.h5')
self.model_predict.save_weights(save_location+'/predicting_model_weights.h5')
def load_network_weights(self, load_location):
self.model_train.load_weights(load_location+'/training_model_weights.h5')
self.model_predict.load_weights(load_location+'/predicting_model_weights.h5')
class QuadBounceBallVPG(object):
def __init__(self, total_episodes=1000, steps_per_episode=200):
self.total_episodes = int(total_episodes)
self.steps_per_episode = int(steps_per_episode)
# print("init", total_episodes, steps_per_episode)
# initialize env and call step////
def execute_episode(self,env,agent,n):
#reset buffer every episode
state_buffer,action_buffer,reward_buffer = [],[],[] # bugger to strore values every step
total_reward = 0
for episode in range(n):
done = False
current_state = env.reset()
# step
while not done:
action_to_execute = agent.get_action(np.reshape(current_state,[1,agent.input_size]))
done,next_state,reward = env.step(action_to_execute)
total_reward += reward
state_buffer.append(current_state)
action_buffer.append(action_to_execute)
reward_buffer.append(reward)
current_state = next_state
# store episode in buffer
state_buffer = np.array(state_buffer)
action_buffer = np.array(action_buffer)
reward_buffer = np.array(agent.get_discounted_rewards(reward_buffer))
loss = agent.fit(state_buffer,action_buffer,reward_buffer)
print("Training on batch complete. Loss: "+ str(loss))
return total_reward/n
def train(self):
log_file = open('vpg_reward_episode.log','w')
env = envi.quadBounceSim()
agent = VanillaPolicyGradient(env)
for episode in range(self.total_episodes):
avg_reward = self.execute_episode(env, agent, self.steps_per_episode)
# print("avg_reward:", avg_reward)
agent.save_network_weights("vpg")
print("episode: "+ str(episode)+", reward: "+str(avg_reward))
log_file.write("episode: "+ str(episode)+", reward: "+str(avg_reward)+"\n")
env.destroy()
def eval(self):
env = envi.quadBounceSim()
agent = VanillaPolicyGradient(env)
agent.load_network_weights("vpg/eval")
for i in range(self.total_episodes):
done = False
total_reward = 0
current_state = env.reset()
while not done:
action_to_execute = agent.get_action(np.reshape(current_state,[1,agent.input_size]))
# print(action_to_execute)
done,next_state,reward = env.step(action_to_execute)
total_reward += reward
current_state = next_state
print("reward obtained "+str(total_reward))
# QuadBounceBallVPG().eval()