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import os
import math
import time
import gym
import random
import utils
import keras
import catch
import catch_v2
import catch_v3
import catch_v4
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):
if environment == "Catch-v0":
self.environment = catch.CatchEnv()
elif environment == "Catch-v2":
self.environment = catch_v2.CatchEnv()
elif environment == "Catch-v3":
self.environment = catch_v3.CatchEnv()
elif environment == "Catch-v4":
self.environment = catch_v4.CatchEnv()
self.approximator = approximator
self.state_size = (84, 84, 4)
self.action_size = 3
self.warm_up_episodes = 50
self.memory = ReplayBuffer(700)
self.last_few = 50
self.batch_size = 32
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_catch_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()
observe, reward, terminal = 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)
next_state, reward, done = self.environment.step(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
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):
if environment == "Catch-v0":
env = catch.CatchEnv()
elif environment == "Catch-v2":
self.environment = catch_v2.CatchEnv()
elif environment == "Catch-v3":
self.environment = catch_v3.CatchEnv()
elif environment == "Catch-v4":
self.environment = catch_v4.CatchEnv()
tot_rewards = []
done = False
dead = False
scores = []
states = []
actions = []
rewards = []
step, score, start_life = 0, 0, 5
observe = env.reset()
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)
next_state, reward, done = env.step(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)
score += reward
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
training_episodes = 10
warm_up_episodes = 10
testing_returns = []
agent = UpsideDownAgent(environment, approximator)
for e in range(training_episodes):
print("Training Episode {}".format(e))
for i in range(100):
agent.train_behaviour_function()
print("Finished training B!")
for i in range(15):
exploratory_commands = agent.sample_exploratory_commands() # Line 5 Algorithm 1
desired_return = exploratory_commands[0]
desired_horizon = exploratory_commands[1]
agent.generate_episode(environment, e, desired_return, desired_horizon, False)
if e % 2 == 0:
for i in range(1):
r = agent.generate_episode(environment, e, desired_return, desired_horizon, True)
testing_returns.append(r)
exploratory_commands = agent.sample_exploratory_commands()
if __name__ == "__main__":
run_experiment()
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