def select_action(state, qnet, epsilon, action_size):
sample = random.random()
if sample > epsilon:
# return qnet(
# state.type(FloatTensor)).detach().max(1)[1].view(1,1)
return select_maxq_action(state, qnet)
else:
return LongTensor([[random.randrange(action_size)]])
next_state = preprocess_state(next_state,state_dim)
#print(state.shape, next_state.shape, reward.shape, done)
# Store the transition in memory
memory.push(state, action, next_state, reward, done, None)
# Move to the next state
state = next_state
n_steps += 1
scores.append(n_steps)
state = np.append(env.reset(), np.random.normal(size = noise_dim))
state = preprocess_state(state, state_dim)
done = False
n_steps = 0
while not done:
# Select and perform an action
action = select_maxq_action(state, qnet)
next_state, reward, done, _ = env.step(action.item())
next_state = np.append(next_state, np.random.normal(size = noise_dim))
next_state = preprocess_state(next_state, state_dim)
state = next_state
n_steps += 1
dp_scores.append(n_steps)
mean_score = np.mean(dp_scores)
# Set the value we want to achieve
if mean_score >= 195 and i_episode >= 30:
print('Ran {} episodes. Solved after {} trials ✔'.format(i_episode, i_episode - 30))
break
if i_episode % 100 == 0:
print('[Episode {}] - Mean survival time over last 100 episodes was {} ticks. Epsilon is {}'
.format(i_episode, mean_score, epsilon))