def __init__(self, parent=None):
tk.Frame.__init__(self, parent)
self.grid(column=0, row=0)
self.columnconfigure(0, weight=1)
self.rowconfigure(0, weight=1)
self.parent = parent
self.test_problem = State()
self.test_fringe = Queue()
self.problem = State()
self.solution = tree_search(self.test_problem, self.test_fringe)
self.solution_stage = 0
self.connor_hp = tk.IntVar()
self.arnold_hp = tk.IntVar()
self.connor_defense = tk.StringVar()
self.arnold_defense = tk.StringVar()
self.update()
name_column = 1
data_column = 2
self.ui_label(1, name_column, 'Connor')
self.ui_label(4, name_column, 'Arnold')
self.ui_label(2, name_column, 'Defense')
self.ui_label(5, name_column, 'Defense')
self.ui_label(1, data_column, self.connor_hp, True)
self.ui_label(4, data_column, self.arnold_hp, True)
self.ui_label(2, data_column, self.connor_defense, True)
self.ui_label(5, data_column, self.arnold_defense, True)
self.ui_btn(6, data_column, 'Next', self.resolve)
def loop(n):
logger_her.info("***************************")
logger_her.info("**** Bit flipping game ****")
logger_her.info("***************************")
logger_her.info("Start main loop with size {}".format(n))
logger_her.info("HER STATUS: {}".format(HER))
actor = QModel(n, HER)
critic = QModel(n, HER)
if not TRAIN_FROM_SCRATCH:
actor.load()
critic.load()
else:
logger_her.info("Training QNetworks from scratch")
re_buffer = Buffer(BUFFER_SIZE)
for epoch in range(EPOCHS):
logger_her.info("Start epoch {}".format(epoch + 1))
for episode_idx in range(EPISODES):
goal = State.sample_status(n)
start = State.sample_status(n)
# here we will going to store start and goal in a state object
state = State(start, goal)
_, episode = sample_episode(actor, state, epsilon_greedy=True)
re_buffer.add(episode)
if HER:
new_experience = []
for s, a, r, sn in episode:
for t in _sample(n, HER_NEW_GOALS):
_g = episode[t][-1].status
_sn = State(sn.status.copy(), _g.copy())
exp = (State(s.status.copy(), _g.copy()), a, 0 if _sn.is_final else -1, _sn)
new_experience.append(exp)
re_buffer.add(new_experience)
for training_step in range(TRAINING_STEPS):
minibatch = re_buffer.sample(BATCH_SIZE)
train(critic, actor, minibatch)
if (epoch + 1) % UPDATE_ACTOR == 0:
actor.update(critic)
success_rate = evaluate_actor(actor)
re_buffer.log_stats()
if success_rate >= 1. - 1e-9:
logger_her.info("Learned policy (QAction-Value) for {} bits in {} epochs".format(n, epoch + 1))
break
def deserialize_json(self, o):
# return s,a,r,sprime,pfbm tuple
s = State.deserialize_json(o["s"])
sprime = State.deserialize_json(o["sprime"])
a = o["a"]
r = o["r"]
pfbm = None if o["pfbm"] is None else np.asarray(o["pfbm"])
return (s, a, r, sprime, pfbm)
def deserialize_json(self, o):
# return s,a,r,sprime,pfbm tuple
s = State.deserialize_json(o["s"])
sprime = State.deserialize_json(o["sprime"])
a = o["a"]
r = o["r"]
pfbm = None if o["pfbm"] is None else np.asarray(o["pfbm"])
return (s,a,r,sprime,pfbm)
class AoasUI(tk.Frame):
def __init__(self, parent=None):
tk.Frame.__init__(self, parent)
self.grid(column=0, row=0)
self.columnconfigure(0, weight=1)
self.rowconfigure(0, weight=1)
self.parent = parent
self.test_problem = State()
self.test_fringe = Queue()
self.problem = State()
self.solution = tree_search(self.test_problem, self.test_fringe)
self.solution_stage = 0
self.connor_hp = tk.IntVar()
self.arnold_hp = tk.IntVar()
self.connor_defense = tk.StringVar()
self.arnold_defense = tk.StringVar()
self.update()
name_column = 1
data_column = 2
self.ui_label(1, name_column, 'Connor')
self.ui_label(4, name_column, 'Arnold')
self.ui_label(2, name_column, 'Defense')
self.ui_label(5, name_column, 'Defense')
self.ui_label(1, data_column, self.connor_hp, True)
self.ui_label(4, data_column, self.arnold_hp, True)
self.ui_label(2, data_column, self.connor_defense, True)
self.ui_label(5, data_column, self.arnold_defense, True)
self.ui_btn(6, data_column, 'Next', self.resolve)
def resolve(self):
if self.solution_stage < len(self.solution):
print(self.solution[self.solution_stage])
self.problem.agent_action(self.solution[self.solution_stage])
self.update()
self.solution_stage += 1
def update(self):
self.connor_hp.set(self.problem.connor.hp)
self.arnold_hp.set(self.problem.terminator.hp)
self.connor_defense.set(self.problem.connor.defense)
self.arnold_defense.set(self.problem.terminator.defense)
def ui_label(self, row, column, text, textvariable=False):
if textvariable == False:
tk.Label(self, text=text).grid(column=column, row=row)
else:
tk.Label(self, textvariable=text).grid(column=column, row=row)
def ui_btn(self, row, column, text, command):
tk.Button(self, text=text, command=command).grid(column=column,
row=row)
def mc_control(num_episodes=10000):
q_sa = {}
p = {}
n_s = {}
n_sa = {}
n0 = 100
for _ in range(num_episodes):
state = State()
reward = 0
episode_s = []
episode_sa = []
while not state.terminal:
s = state.as_tuple()
if s in p:
a = sample_action(p[s])
else:
a = Action.random()
episode_s.append(s)
episode_sa.append(s + (a, ))
state, reward = step(state, a)
ns = n_s.get(s, 0)
n_s[s] = ns + 1
sa = s + (a, )
nsa = n_sa.get(sa, 0)
n_sa[sa] = nsa + 1
# GLIE MC Control
for sa in set(episode_sa):
nsa = n_sa[sa]
qsa = q_sa.get(sa, 0)
q_sa[sa] = qsa + ((reward - qsa) / nsa)
# Improve policy
for s in set(episode_s):
a_best = greedy_action(q_sa, s)
ns = n_s.get(s, 0)
epsilon = n0 / (n0 + ns)
selection_probs = []
for a in list(Action):
if a is a_best:
selection_probs.append(1 - epsilon + epsilon / len(Action))
else:
selection_probs.append(epsilon / len(Action))
p[s] = selection_probs
return q_sa
def expand_Q(w):
Q = np.zeros((10, 21, 2))
for dealer in DEALER_RANGE:
for player in PLAYER_RANGE:
for action in ACTIONS:
#state = (dealer, player)
state = State()
state.dealercard = dealer
state.playersum = player
feats = phi(state, action)
Q[dealer - 1, player - 1][action] = np.sum(feats * w)
return Q
def evaluate_actor(actor, episodes_count=TESTING_EPISODES, verbose=0, pause=0):
success_counter = 0
for episode_ev in range(episodes_count):
start = State.sample_status(actor.n)
goal = State.sample_status(actor.n)
success, _ = sample_episode(actor, State(start, goal), epsilon_greedy=False, verbose=verbose)
success_counter += int(success)
if pause: input("Press <Enter> to continue...")
logger_her.info("Success/Total {}/{}".format(success_counter, episodes_count))
logger_her.info("Success rate: {}".format(success_counter / episodes_count))
return success_counter / episodes_count
def get_value_function(self):
for i in range(1, self.env.dealer_max_value + 1):
for j in range(1, self.env.agent_max_value + 1):
s = State(j, i)
print(s.dealer_sum, s.agent_sum)
self.V[i][j] = self.get_max_action(s)
return self.V
def __init__(self,
states,
alpha: float = 0.15,
random_factor: float = 0.2):
self.state_history = [(State(0, 0), 0)]
self.alpha = alpha
self.random_factor = random_factor
self.G = Agent.init_reward(states)
def _load_task(self, task_dict, states_dir):
task = Task(resume_utg=False, **task_dict)
for i in range(len(task_dict["state_history"])):
state_str = task_dict["state_history"][i]
action_str = task_dict["action_history"][i]
state = State.load(state_dir=states_dir, state_str=state_str)
state.setup(task)
action = self._load_action(state, action_str)
task.state_history.append(state)
task.action_history.append(action)
task.state = State.load(state_dir=states_dir,
state_str=task_dict["state"])
task.state.setup(task)
task.reward = task_dict["reward"]
task.total_reward = task_dict["total_reward"]
task.done = task_dict["done"]
return task
def Lfa():
lmbd = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
learning_curves = {}
state1 = State()
num_episodes = 2000
with open("./Q_dump_episodes_1000000.pkl", "rb") as f:
opt_value = pickle.load(f)
for item in lmbd:
state1.dealercard = random.randint(1,10)
state1.playersum = random.randint(1,10)
Q_value,error_history = lfa_learn(item,opt_value,num_episodes)
learning_curves[item] = error_history
plot_file = ("./outcome/lfa_error_{}_episodes_time_{}.pdf".format(20000,time.time()))
plot_learning_curve(learning_curves, save=plot_file)
def sarsa_lambda(num_episodes=1000, lamba=0, gamma=1, yield_progress=False):
q_sa = {}
n_s = {}
n_sa = {}
for n in range(num_episodes):
e_sa = {}
state = State()
s = state.as_tuple()
a = epsilon_greedy_action(q_sa, s, calculate_epsilon(n_s, s))
while not state.terminal:
state, reward = step(state, a)
n_s[s] = n_s.get(s, 0) + 1
s_next = state.as_tuple()
a_next = epsilon_greedy_action(q_sa, s_next,
calculate_epsilon(n_s, s_next))
sa = s + (a, )
sa_next = s_next + (a_next, )
qsa = q_sa.get(sa, 0)
qsa_next = q_sa.get(sa_next, 0)
nsa = n_sa.get(sa, 0) + 1
n_sa[sa] = nsa
delta = reward + gamma * qsa_next - qsa
e_sa[sa] = e_sa.get(sa, 0) + 1
for (s, a) in generate_all_state_action_pairs():
sa = s + (a, )
q_sa[sa] = q_sa.get(sa, 0) + (delta * e_sa.get(sa, 0)) / nsa
e_sa[sa] = gamma * lamba * e_sa.get(sa, 0)
s = s_next
a = a_next
if yield_progress:
yield n + 1, q_sa
if not yield_progress:
yield num_episodes, q_sa
def Sarsa_lamda_Control(lmbd, opt_value, num_episodes):
#initialize
value = np.zeros((10, 21, 2))
counter = np.zeros((10, 21, 2))
totalreward = 0
error_history = []
for episode in range(1, num_episodes + 1):
# initialize env
state1 = State()
state1.dealercard = random.randint(1, 10)
state1.playersum = random.randint(1, 10)
E = np.zeros((10, 21, 2))
while state1 != "terminal":
action1 = Epsilon_greedy_policy(value, counter, state1)
state2, reward = Step(state1, action1)
idx1 = (state1.dealercard - 1, state1.playersum - 1, action1)
Q1 = value[idx1]
if state2 == "terminal":
Q2 = 0.0
else:
action2 = Policy(value, counter, state2)
idx2 = (state2.dealercard - 1, state2.playersum - 1, action2)
Q2 = value[idx2]
counter[idx1] += 1
E[idx1] += 1
alpha = 1.0 / counter[idx1]
delta = reward + GAMMA * Q2 - Q1
value += alpha * delta * E
E *= GAMMA * lmbd
state1 = state2
error_history.append((episode, mse(value, opt_value)))
return value, error_history
def Sarsa():
lmbd = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]
#lmbd = [0.1]
learning_curves = {}
state1 = State()
num_episodes = 20000
with open("./Q_dump_episodes_1000000.pkl", "rb") as f:
opt_value = pickle.load(f)
for item in lmbd:
#print("in main, item:",item)
state1.dealercard = random.randint(1,10)
state1.playersum = random.randint(1,10)
#print("state in main:",state1.dealercard,state1.playersum)
Q_value,error_history = Sarsa_lamda_Control(item,opt_value,num_episodes)
learning_curves[item] = error_history
#print("learning_curves:",learning_curves)
plot_file = ("./outcome/Sarsa_error_{}_episodes_time_{}.pdf".format(20000,time.time()))
plot_learning_curve(learning_curves, save=plot_file)
def compute_reward(task, trace_lines):
# logging.info(f"compute_reward starts at {datetime.now()}")
states = []
actions = []
# browser.reset(task.start_url)
state_action_lines = [(line[:(line.find(": "))],
line[(line.find(": ") + 2):])
for line in trace_lines]
current_state_str, action_line = state_action_lines[0]
current_state = State.load(states_dir, current_state_str)
actions.append("RESET")
states.append(current_state)
task.reset(current_state, update_utg=False)
last_action = load_action(current_state, action_line)
actions.append(action_line)
end_reached = False
correct_rewards = [0]
incorrect_rewards = [task.total_reward]
for state_str, action_line in state_action_lines[1:]:
current_state = State.load(states_dir, state_str)
states.append(current_state)
task.update(last_action, current_state, update_utg=False)
if task.target_achieved:
correct_rewards.append(task.total_reward)
else:
incorrect_rewards.append(task.total_reward)
if action_line == "END":
end_reached = True
break
else:
last_action = load_action(current_state, action_line)
max_correct_reward = max(correct_rewards)
max_incorrect_reward = max(incorrect_rewards)
logging.info(
f" task got correct reward {max_correct_reward:6.3f}"
f" and incorrect reward {max_incorrect_reward:3.3f}: {task.name}"
)
return max_correct_reward, max_incorrect_reward
def test_step_normal_Q_learning():
# Test case when there is a previous state and action but the game
# is not done. Check that Q is updated correctly in the
# deterministic greedy case (epsilon=0).
alpha = 0.1
gamma = 0.9
agent = TDAgent(0, alpha=alpha, gamma=gamma, epsilon0=0, method='q-learning')
agent.prev_action = (1,1)
agent.prev_state = State()
prev_afterstate = agent.prev_state.put(agent.prev_action, 0)
# Construct a Q function to force a spefic update.
# Value before learning.
q_prev = 0.6
agent.Q[prev_afterstate] = q_prev
curr_state = State().put((1,1), 0).put((0,0), 1)
# Create two possible actions, one better than the other. The
# agent should choose to use the value of taking action (2,2) for
# the target value.
agent.Q[curr_state.put((2,2), 0)] = 0.7
agent.Q[curr_state.put((0,2), 0)] = 0.5
future_return = gamma * 0.7
# Take a step. We are not done, but give a nonzero reward to
# check that it is used.
reward = 1
done = False
action = agent.step(curr_state, reward, done)
# Value after learning
q_curr = agent.Q[prev_afterstate]
print q_prev, q_curr
assert q_curr == q_prev + alpha * (
reward + future_return - q_prev
)
def lfa_learn(lmbd, opt_value, num_episodes):
#initialize
Q = np.zeros((10, 21, 2))
counter = np.zeros((10, 21, 2))
totalreward = 0
error_history = []
w = (np.random.rand(*FEATS_SHAPE) - 0.5) * 0.001
for episode in range(1, num_episodes + 1):
# initialize env
state1 = State()
state1.dealercard = random.randint(1, 10)
state1.playersum = random.randint(1, 10)
#state1 = (state1.dealercard,state1.playersum)
E = np.zeros_like(w)
while state1 != "terminal":
Qhat1, action1 = policy(state1, w)
state2, reward = Step(state1, action1)
Qhat2, action2 = policy(state2, w)
feats1 = phi(state1, action1)
grad_w_Qhat1 = feats1
delta = reward + GAMMA * Qhat2 - Qhat1
E = GAMMA * lmbd * E + grad_w_Qhat1
dw = ALPHA * delta * E
w += dw
state1 = state2
Q = expand_Q(w)
#print("in lfa while")
error_history.append((episode, mse(Q, opt_value)))
return Q, error_history
def choose_action(self, state: State,
allowed_moves: List[Action]) -> Action:
max_G = -10e15
next_move = None
random_N = np.random.random()
if random_N < self.random_factor:
next_move = np.random.choice(allowed_moves)
else:
for action in allowed_moves:
y = Maze.action_space[action].dy
x = Maze.action_space[action].dx
new_state = State(state.x + x, state.y + y)
if self.G[new_state] >= max_G:
next_move = action
max_G = self.G[new_state]
return next_move
def trial(robot: Agent) -> List[int]:
maze = Maze()
move_history = []
for i in range(5000):
if i % 1000 == 0:
print(i)
while not maze.is_complete():
state, _ = maze.get_state_and_reward()
action = robot.choose_action(state, maze.allowed_states[state])
maze.update_maze(action)
state, reward = maze.get_state_and_reward()
robot.update_state_history(state, reward)
if maze.steps > 1000:
maze.robot_position = State(5, 5)
robot.learn()
move_history.append(maze.steps)
maze.reset()
return move_history
def successor_function(state):
action = state.functions
result = []
# Simulate the actions, then return the state of the 2 actions
for x in range(len(action)):
# Create a new state with new values, very important so that the address in the RAM will be new
new_state = State()
new_state.connor.hp = state.connor.hp
new_state.terminator.hp = state.terminator.hp
new_state.terminator.defense = state.terminator.defense
connor = new_state.connor
terminator = new_state.terminator
# Simulate the actions then append the result in new_state
if action[x] == 'attack':
connor.attack(terminator)
result.append(new_state)
else:
connor.defend(terminator)
result.append(new_state)
return action, result
def lfa_sarsa_lambda(num_episodes=1000, lamba=0, gamma=1, alpha=0.01, yield_progress=False):
# Set up the coarse codes, initial weights.
action_codes = {}
for action in list(Action):
action_fns = []
for dealer_interval in [(1,4), (4,7), (7,10)]:
for player_interval in [(1,6), (4,9), (7,12), (10,15), (13,18), (16,21)]:
cuboid_fn = create_cuboid_fn(dealer_interval, player_interval, action)
action_fns.append(cuboid_fn)
action_codes[action] = action_fns
def greedy(s, w):
p, d = s
action_values = []
for a in list(Action):
value = 0
for cuboid_fn in action_codes[a]:
if cuboid_fn(p, d, a):
value += w.get(cuboid_fn, 0)
action_values.append((a, value))
action_values.sort(key=itemgetter(1), reverse=True)
return action_values[0][0]
def e_greedy(s, w, epsilon=0.05):
a_best = greedy(s, w)
selection_probs = []
default_p = epsilon / len(Action)
for a in list(Action):
if a is a_best:
selection_probs.append(1 - epsilon + default_p)
else:
selection_probs.append(default_p)
return sample_action(selection_probs)
def f_sa(s, a):
p, d = s
for cuboid_fn in action_codes[a]:
if cuboid_fn(p, d, a):
yield cuboid_fn
def compile_q_sa(w):
q_sa = {}
for (p, d), a in generate_all_state_action_pairs():
sa = (p, d, a)
val = 0
for i in f_sa((p, d), a):
val += w.get(i, 0)
q_sa[sa] = val
return q_sa
w_f = {}
for n in range(num_episodes):
state = State()
s = state.as_tuple()
a = e_greedy(s, w_f)
z_f = {}
while not state.terminal:
state, reward = step(state, a)
delta = reward
for i in f_sa(s, a):
delta = delta - w_f.get(i, 0)
z_f[i] = z_f.get(i, 0) + 1
if state.terminal:
for i, zi in z_f.items():
w_f[i] = w_f.get(i, 0) + alpha * delta * zi
break
s_next = state.as_tuple()
a_next = e_greedy(s_next, w_f)
for i in f_sa(s_next, a_next):
delta = delta + gamma * w_f.get(i, 0)
for i, zi in z_f.items():
w_f[i] = w_f.get(i, 0) + alpha * delta * zi
z_f[i] = gamma * lamba * zi
s = s_next
a = a_next
if yield_progress:
yield n+1, compile_q_sa(w_f)
if not yield_progress:
yield num_episodes, compile_q_sa(w_f)
from environment import State
from agents import ComputerPlayer, HumanPlayer
if __name__ == "__main__":
# play with human
p1 = ComputerPlayer("computer", exp_rate=0)
p1.load_policy("policy_p1")
p2 = HumanPlayer("human")
st = State(p1, p2)
st.play_with_human()
from environment import State
from agents import ComputerPlayer
if __name__ == "__main__":
# training
p1 = ComputerPlayer("p1")
p2 = ComputerPlayer("p2")
st = State(p1, p2)
print("training...")
st.play_with_ai(50000)
p1.save_policy()
p2.save_policy()
from environment import State
from utils import Queue
from ai import bfs, ids, best_fs, a_star_search
from ui import AoasUI
import tkinter as tk
from tkinter import ttk
if __name__ == '__main__':
# Initialize all important things
problem = State()
fringe = Queue()
next_gen = Queue()
depth = 3
count = 0
saved_input = None
user_input = None
# Manual algorithm switching
manual = False
# While the solution has not been found, do all of this
solved = False
while solved == False:
# saved_input to automate the entire thing for long processes
if saved_input == None and manual == False:
saved_input = input(
'"1" for BFS, "2" for IDS, "3" for Greedy BFS, "4" for A* Search: '
)
elif manual == True:
saved_input = input(
from environment import State
from Net_pg import PolicyGradient
import numpy as np
N = 20
env = State()
RL = PolicyGradient(
n_actions=env.n_actions,
n_features=env.n_features,
learning_rate=0.01,
reward_decay=0.99,
)
fid_max = 0
for episode in range(500):
observation = env.reset()
for ii in range(N):
action = RL.choose_action(observation)
observation_, reward, done, fid = env.step(action, ii)
RL.store_transition(observation, action, reward)
observation = observation_
if done or ii >= N - 1:
break
if episode >= 490:
if fid > fid_max: