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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
class ReplayBuffer():
"""
Thank you: https://github.com/BY571/
"""
def __init__(self, max_size):
self.max_size = max_size
self.buffer = np.load('./buffers/CartPole-v0/1/memory_buffer.npy')
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):
self.environment = gym.make(environment)
self.state_size = self.environment.observation_space.shape[0]
self.action_size = self.environment.action_space.n
self.memory = ReplayBuffer(700)
self.last_few = 75
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.testing_state = 0
self.behaviour_function = utils.get_functional_behaviour_function(self.state_size, self.command_size, self.action_size, False)
self.testing_rewards = []
def get_action(self, observation, command):
"""
We will sample from the action distribution modeled by the Behavior Function
"""
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))
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 = episode['states'][t1]
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)
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
score = 0
state = env.reset()
scores = []
states = []
actions = []
rewards = []
while not done:
state = np.reshape(state, [1, self.state_size])
states.append(state)
observation = state
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(observation, command)
actions.append(action)
else:
action = self.get_greedy_action(observation, command)
next_state, reward, done, info = env.step(action)
next_state = np.reshape(next_state, [1, self.state_size])
rewards.append(reward)
score += reward
state = next_state
desired_return -= reward # Line 8 Algorithm 2
desired_horizon -= 1 # Line 9 Algorithm 2
desired_horizon = np.maximum(desired_horizon, 1)
self.testing_rewards.append(score)
if testing:
print('Querying the model ...')
print('Testing score: {}'.format(score))
return score
def run_experiment():
environment = 'CartPole-v0'
seed = 1
episodes = 500
returns = []
agent = UpsideDownAgent(environment)
for e in range(episodes):
for i in range(100):
agent.train_behaviour_function()
for i in range(15):
tmp_r = []
r = agent.generate_episode(environment, e, 200, 200, False)
tmp_r.append(r)
print(np.mean(tmp_r))
returns.append(np.mean(tmp_r))
agent.generate_episode(environment, 1, 200, 200, True)
utils.save_offline_results(environment, approximator, seed, returns)
if __name__ == "__main__":
run_experiment()
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