class RL_Agent():
def __init__(self, episode_size=150):
self.model = MLP((SCREEN_HEIGHT_g, SCREEN_WIDTH_g), 300)
#self.load("models/model_1185.npz")
self.activations = []
self.frames = []
self.states_alive = []
self.episode_size = episode_size
self.episode_decisions = np.zeros((8))
self.episodes_wins = 0
self.episodes_nb = 0
self.iter = 0
def explore_exploit(self, prediction):
choice = np.random.rand()
if choice <= .8:
return prediction
return one_hot(round(np.random.rand() * 7))
def update(self, frame, is_dead):
frame = preprocessing(frame)
act_h, y_pred = self.model.forward_keep_activations(frame)
y_pred = self.explore_exploit(y_pred)
self.activations.append((act_h, y_pred))
self.frames.append(frame)
#print(y_pred)
self.episode_decisions += one_hot(y_pred)
self.iter += 1
if is_dead or self.iter == self.episode_size:
for activation, frame in zip(self.activations, self.frames):
if is_dead: # Agent is dead
#y_true = np.array([0, 0, 0, 0.5, 0.5, 0, 0, 0])
y_true = wrong_move(one_hot(activation[1]))
#print("\n", activation[1],'\n', y_true, '\n\n')
grads = self.model.gradients(frame, activation[0],
activation[1], y_true)
self.model.backward(grads)
else:
y_true = one_hot(activation[1])
grads = self.model.gradients(frame, activation[0],
activation[1], y_true)
self.model.backward(grads)
print(
f"Episode #{self.episodes_nb} : {'LOSE' if is_dead else 'WIN'} Decisions : {self.episode_decisions}"
)
self.reset()
self.episodes_nb += 1
self.episode_decisions.fill(0)
if not is_dead:
self.episodes_wins += 1
if self.episodes_nb % 100 == 0:
print(f"Last 100 episodes : {self.episodes_wins}%")
self.episodes_wins = 0
return np.argmax(y_pred)
def reset(self):
self.activations = []
self.iter = 0
def load(self, path):
loaded = np.load(path)
self.model.Wh = loaded['Wh']
self.model.Bh = loaded['Bh']
self.model.Wo = loaded['Wo']
self.model.Bo = loaded['Bo']
def save(self):
np.savez_compressed("models/model_" + str(self.episodes_nb),
Wh=self.model.Wh,
Bh=self.model.Bh,
Wo=self.model.Wo,
Bo=self.model.Bo)
