sigma_average_dict = defaultdict(list)
components = ['W: first hidden', 'b: first hidden', 'W: second hidden', 'b: second hidden', \
'W: output','b: output']
for i_episode in range(num_episodes):
# Initialize the environment and state
env.reset()
state = Tensor(env.get_state()).unsqueeze(0)
score = 0
for t in xrange(500):
# Select and perform an action
if t % sample_period == 0:
w_sample = model.sample()
action = select_action(state)
reward, done = env.do_action(action[0, 0])
score += reward
reward = Tensor([reward])
# Observe new state
if not done:
next_state = Tensor(env.get_state()).unsqueeze(0)
else:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
if done:
