def run_simulation(game_map: Map, Q: dict, epsilon_greedy=max_first):
display = Display(game_map)
game_map.reset()
state = deepcopy(game_map.get_init_state())
run = True
score = 0
display.render()
while run:
action = epsilon_greedy(Q, state,
game_map.get_available_actions(state), 0.3,
"softmax")
state, _ = game_map.apply_action(action)
run = not Display.is_close() and not game_map.is_final(score)
display.render()
Display.close_display()
if game_map.win():
print("Game Win!!!")
else:
print("Game Lose!!!")
def sarsa(game_map,
epsilon_greedy,
Q={},
learning_rate=0.1,
discovering_factor=0.9,
epsilon=0.3,
training_episode=1000,
evaluation_episode=10,
strategy="default",
verbose=False):
train_scores = []
eval_scores = []
initial_state = game_map.get_init_state()
win_game = 0
q_values = []
number_cell_visited = []
if verbose:
display = Display(game_map)
for train_ep in range(1, training_episode + 1):
game_map.reset()
score = 0
state = deepcopy(initial_state)
actions = game_map.get_available_actions_mouse()
action = epsilon_greedy(Q, state, actions, epsilon, strategy)
if verbose:
from src.map import ACTIONS
import sys
print_q(Q, game_map.height, game_map.width, ACTIONS)
display.render()
sys.stdin.readline()
if display.is_close():
sys.exit(0)
while not game_map.is_final(score):
# apply action and get the next state and the reward
next_state, reward = game_map.apply_action(action)
score += reward
state_q_value, number_visited = Q.get((state, action), (0, 0))
action_next_state = epsilon_greedy(
Q, next_state, game_map.get_available_actions(next_state),
epsilon, strategy)
next_state_q_value, _ = Q.get((next_state, action_next_state),
(0, 0))
q_value = state_q_value + learning_rate * (
reward + discovering_factor * next_state_q_value -
state_q_value)
Q[(state, action)] = (q_value, number_visited + 1)
state = next_state
action = action_next_state
if verbose:
from src.map import ACTIONS
import sys
print_q(Q, game_map.height, game_map.width, ACTIONS)
display.render()
sys.stdin.readline()
if display.is_close():
sys.exit(0)
q_values.append(sum([x[Q_VALUE] for x in Q.values()]))
win_game += int(game_map.win())
train_scores.append(score)
number_cell_visited.append(len(Q.keys()))
print("Episode {}/{}, score : {} win : {}".format(
train_ep, training_episode, score, game_map.win()))
if train_ep % evaluation_episode == 0:
avg_score = mean(train_scores[train_ep -
evaluation_episode:train_ep])
eval_scores.append(avg_score)
win_game /= training_episode
return Q, train_scores, eval_scores, win_game, q_values, number_cell_visited
