model.load(model_filename)
with open(results_filename, 'rb') as input:
res = pickle.load(input)
epsilon = res.epsilon
if opt.epsilon < 1:
epsilon = opt.epsilon
epsilons = np.maximum(
np.arange(epsilon, epsilon - 1 + min_epsilon,
(-1 + min_epsilon) / n_train), min_epsilon)
else:
epsilons = np.arange(1, min_epsilon, (-1 + min_epsilon) / n_train)
# size of memory
for i_train in range(n_train):
epsilon = epsilons[i_train]
res.epsilon = epsilon
print("Training: round {}, epsilon = {}".format(i_train, round(epsilon,
2)))
lengths_i_train = []
scores_i_train = []
for i_episode in range(n_episodes):
i = 0
done = False
grid = env.reset()
grid = grid.reshape((1, n_channels, env.nrow, env.ncol))
t0 = time.time()
while i < imax:
i += 1
source = grid.copy()
if epsilon >= np.random.rand():
action = np.random.randint(4)
