File size: 10,277 Bytes
b49af5c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 |
import os
import math
import time
import gym
import random
import utils
import keras
import numpy as np
from collections import deque
from matplotlib import pyplot as plt
from sklearn.preprocessing import OneHotEncoder
class ReplayBuffer():
"""
Thank you: https://github.com/BY571/
"""
def __init__(self, max_size):
self.max_size = max_size
self.buffer = []
def add_sample(self, states, actions, rewards):
episode = {"states": states, "actions":actions, "rewards": rewards, "summed_rewards":sum(rewards)}
self.buffer.append(episode)
def sort(self):
#sort buffer
self.buffer = sorted(self.buffer, key = lambda i: i["summed_rewards"],reverse=True)
# keep the max buffer size
self.buffer = self.buffer[:self.max_size]
def get_random_samples(self, batch_size):
self.sort()
idxs = np.random.randint(0, len(self.buffer), batch_size)
batch = [self.buffer[idx] for idx in idxs]
return batch
def get_n_best(self, n):
self.sort()
return self.buffer[:n]
def __len__(self):
return len(self.buffer)
class UpsideDownAgent():
def __init__(self, environment, approximator):
self.environment = gym.make(environment)
self.approximator = approximator
self.state_size = (84, 84, 4)
self.action_size = 3
self.warm_up_episodes = 1 #50
self.render = False
self.memory = ReplayBuffer(700)
self.last_few = 50
self.batch_size = 256
self.command_size = 2 # desired return + desired horizon
self.desired_return = 1
self.desired_horizon = 1
self.horizon_scale = 0.02
self.return_scale = 0.02
self.behaviour_function = utils.get_atari_behaviour_function(self.action_size)
self.testing_rewards = []
self.warm_up_buffer()
def warm_up_buffer(self):
print('Warming up')
for i in range(self.warm_up_episodes):
states = []
rewards = []
actions = []
dead = False
done = False
desired_return = 1
desired_horizon = 1
step, score, start_life = 0, 0, 5
observe = self.environment.reset()
for _ in range(random.randint(1, 30)):
observe, _, _, _ = self.environment.step(1)
state = utils.pre_processing(observe)
history = np.stack((state, state, state, state), axis=2)
history = np.reshape([history], (1, 84, 84, 4))
while not done:
states.append(history)
command = np.asarray([desired_return * self.return_scale, desired_horizon * self.horizon_scale])
command = np.reshape(command, [1, len(command)])
action = self.get_action(history, command)
actions.append(action)
if action == 0:
real_action = 1
elif action == 1:
real_action = 2
else:
real_action = 3
next_state, reward, done, info = self.environment.step(real_action)
next_state = utils.pre_processing(observe)
next_state = np.reshape([next_state], (1, 84, 84, 1))
next_history = np.append(next_state, history[:, :, :, :3], axis = 3)
rewards.append(reward)
state = next_state
if start_life > info['ale.lives']:
dead = True
start_lide = info['ale.lives']
if dead:
dead = False
else:
history = next_history
desired_return -= reward # Line 8 Algorithm 2
desired_horizon -= 1 # Line 9 Algorithm 2
desired_horizon = np.maximum(desired_horizon, 1)
self.memory.add_sample(states, actions, rewards)
def get_action(self, observation, command):
"""
We will sample from the action distribution modeled by the Behavior Function
"""
observation = np.float32(observation / 255.0)
action_probs = self.behaviour_function.predict([observation, command])
action = np.random.choice(np.arange(0, self.action_size), p=action_probs[0])
return action
def get_greedy_action(self, observation, command):
action_probs = self.behaviour_function.predict([observation, command])
action = np.argmax(action_probs)
return action
def train_behaviour_function(self):
random_episodes = self.memory.get_random_samples(self.batch_size)
training_observations = np.zeros((self.batch_size, self.state_size[0], self.state_size[1], self.state_size[2]))
training_commands = np.zeros((self.batch_size, 2))
y = []
for idx, episode in enumerate(random_episodes):
T = len(episode['states'])
t1 = np.random.randint(0, T-1)
t2 = np.random.randint(t1+1, T)
state = np.float32(episode['states'][t1] / 255.)
desired_return = sum(episode["rewards"][t1:t2])
desired_horizon = t2 -t1
target = episode['actions'][t1]
training_observations[idx] = state[0]
training_commands[idx] = np.asarray([desired_return*self.return_scale, desired_horizon*self.horizon_scale])
y.append(target)
_y = keras.utils.to_categorical(y, num_classes=self.action_size)
self.behaviour_function.fit([training_observations, training_commands], _y, verbose=0)
def sample_exploratory_commands(self):
best_episodes = self.memory.get_n_best(self.last_few)
exploratory_desired_horizon = np.mean([len(i["states"]) for i in best_episodes])
returns = [i["summed_rewards"] for i in best_episodes]
exploratory_desired_returns = np.random.uniform(np.mean(returns), np.mean(returns)+np.std(returns))
return [exploratory_desired_returns, exploratory_desired_horizon]
def generate_episode(self, environment, e, desired_return, desired_horizon, testing):
env = gym.make(environment)
tot_rewards = []
done = False
dead = False
scores = []
states = []
actions = []
rewards = []
step, score, start_life = 0, 0, 5
observe = env.reset()
for _ in range(random.randint(1, 30)):
observe, _, _, _ = env.step(1)
state = utils.pre_processing(observe)
history = np.stack((state, state, state, state), axis=2)
history = np.reshape([history], (1, 84, 84, 4))
while not done:
states.append(history)
command = np.asarray([desired_return * self.return_scale, desired_horizon * self.horizon_scale])
command = np.reshape(command, [1, len(command)])
if not testing:
action = self.get_action(history, command)
actions.append(action)
else:
action = self.get_greedy_action(history, command)
if action == 0:
real_action = 1
elif action == 1:
real_action = 2
else:
real_action = 3
next_state, reward, done, info = env.step(real_action)
next_state = utils.pre_processing(observe)
next_state = np.reshape([next_state], (1, 84, 84, 1))
next_history = np.append(next_state, history[:, :, :, :3], axis = 3)
clipped_reward = np.clip(reward, -1, 1)
rewards.append(clipped_reward)
score += reward
if start_life > info['ale.lives']:
dead = True
start_life = info['ale.lives']
if dead:
dead = False
else:
history = next_history
desired_return -= reward # Line 8 Algorithm 2
desired_horizon -= 1 # Line 9 Algorithm 2
desired_horizon = np.maximum(desired_horizon, 1)
self.memory.add_sample(states, actions, rewards)
self.testing_rewards.append(score)
if testing:
print('Querying the model ...')
print('Testing score: {}'.format(score))
return score
def run_experiment():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--approximator', type=str, default='neural_network')
parser.add_argument('--environment', type=str, default='PongDeterministic-v4')
parser.add_argument('--seed', type=int, default=1)
args = parser.parse_args()
approximator = args.approximator
environment = args.environment
seed = args.seed
episodes = 1500
returns = []
agent = UpsideDownAgent(environment, approximator)
for e in range(episodes):
print("Episode {}".format(e))
for i in range(100):
agent.train_behaviour_function()
print("Finished training B!")
for i in range(15):
tmp_r = []
exploratory_commands = agent.sample_exploratory_commands() # Line 5 Algorithm 1
desired_return = exploratory_commands[0]
desired_horizon = exploratory_commands[1]
r = agent.generate_episode(environment, e, desired_return, desired_horizon, False)
tmp_r.append(r)
print(np.mean(tmp_r))
returns.append(np.mean(tmp_r))
exploratory_commands = agent.sample_exploratory_commands()
#agent.generate_episode(environment, 1, 200, 200, True)
utils.save_results(environment, approximator, seed, returns)
if approximator == 'neural_network':
utils.save_trained_model(environment, seed, agent.behaviour_function)
plt.plot(returns)
plt.show()
if __name__ == "__main__":
run_experiment()
|