def verify_network(agent, utils, config):
"""
For plotting - plot the math mean error versions along with the agent mean
"""
experience = namedtuple('experience',
field_names=[
'state', 'value', 'log_prob', 'action',
'reward', 'next_state'
])
# Create two instances of gyms
gym_network = Gym(config.total_routes, config.num_reset_routes, utils)
gym_math = Gym(config.total_routes, config.num_reset_routes, utils)
# tic = time.time()
# Load weights of trained model
agent.load_weights(config.checkpoint_path)
# Collections
math_means = []
math_stds = []
means = []
stds = []
scores_window = deque(maxlen=100)
math_window = deque(maxlen=100)
for e in range(1, 11):
trajectory = []
rewards = []
math_rewards = []
state = gym_network.reset()
math_state = gym_math.reset()
math_loss = [gym_math.loss]
network_loss = [gym_network.loss]
for t in range(config.tmax):
suggestion, log_prob, value = agent.act(state)
route = utils.route_from_suggestion(suggestion)
next_state, reward = gym_network.step(route)
# Compare with math
math_route = utils.deterministic_route(math_state)
math_next_state, math_reward = gym_math.step(math_route)
math_rewards.append(math_reward)
math_loss.append(gym_math.loss)
# Record (s,a,r,s)
exp = experience(state, value, log_prob, suggestion, reward,
next_state)
trajectory.append(exp)
rewards.append(reward)
network_loss.append(gym_network.loss)
state = next_state
scores_window.append(np.sum(rewards))
means.append(np.mean(scores_window))
stds.append(np.std(scores_window))
math_window.append(np.sum(math_rewards))
math_means.append(np.mean(math_window))
math_stds.append(np.std(math_window))
# Compare network vs math
if e == 1:
plot_episode(math_loss, name="Math single episode")
plot_episode(network_loss, name="Network single episode")
if e % 5 == 0:
plot(means, stds, name=config.name, game='RouteMuse')
plot(math_means, math_stds, name='Math', game='RouteMuse')
def train_network(agent, utils, config):
"""
For plotting - plot the math mean error versions along with the agent mean
"""
experience = namedtuple('experience',
field_names=[
'state', 'value', 'log_prob', 'action',
'reward', 'next_state'
])
# Compared training with math baseline
gym = Gym(config.total_routes, config.num_reset_routes, utils)
math_gym = Gym(config.total_routes, config.num_reset_routes, utils)
tic = time.time()
# Collections
max_mean = 0
means = []
stds = []
mins = []
maxes = []
stds = []
steps = []
math_means = []
math_stds = []
math_window = deque(maxlen=100)
scores_window = deque(maxlen=100)
for e in range(1, config.episodes):
trajectory = []
rewards = []
math_rewards = []
state = gym.reset()
math_state = math_gym.reset()
for t in range(config.tmax):
# Normalize state for network
normalized_state = normalize(state)
suggestion, log_prob, value = agent.act(normalized_state)
route = utils.route_from_suggestion(suggestion)
next_state, reward = gym.step(route)
# math comparison
math_route = utils.deterministic_route(math_state)
math_next_state, math_reward = math_gym.step(math_route)
math_rewards.append(math_reward)
math_state = math_next_state
# Record (s,a,r,s)
exp = experience(state, value, log_prob, suggestion, reward,
next_state)
trajectory.append(exp)
rewards.append(reward)
state = next_state
agent.step(trajectory)
steps.append(t)
math_window.append(sum(math_rewards))
math_means.append(np.mean(math_window))
math_stds.append(np.std(math_window))
scores_window.append(sum(rewards))
means.append(np.mean(scores_window))
mins.append(np.min(scores_window))
maxes.append(np.max(scores_window))
stds.append(np.std(scores_window))
if e % 10 == 0:
toc = time.time()
r_mean = np.mean(scores_window)
r_max = max(scores_window)
r_min = min(scores_window)
r_std = np.std(scores_window)
plot(math_means, math_stds, name="Math", game="RouteMuse")
plot(means, stds, name=config.name, game='RouteMuse')
print(
"\rEpisode: {} out of {}, Steps {}, Mean steps {:.2f}, Rewards: mean {:.2f}, min {:.2f}, max {:.2f}, std {:.2f}, Elapsed {:.2f}"
.format(e, config.episodes, np.sum(steps), np.mean(steps),
r_mean, r_min, r_max, r_std, (toc - tic) / 60))
# save scores
if r_mean > max_mean:
pickle.dump([means, maxes, mins],
open(str(config.name) + '_scores.p', 'wb'))
# save policy
agent.save_weights(config.checkpoint_path)
max_mean = r_mean
