def performer(solver, args, render=False):
my_simulator = SIMULATOR()
state = my_simulator.reset()
episode_reward = 0
if render:
my_simulator.render()
for i in range(MAX_EPISODE_LENGTH):
action, _ = solver.search(state, args)
state, reward, terminal = my_simulator.step(action)
if render:
print(SIMULATOR.ACTIONS[action], reward)
my_simulator.render()
episode_reward += reward
if terminal:
break
return episode_reward
def run_exper(model, steps, get_features, pre_proc_features):
from environment import SIMULATOR
# initializing our environment
my_sim = SIMULATOR()
# beginning of an episode
state_temp = my_sim.reset()
observation = my_sim.state_to_tensor(state_temp)
r_tup, e_tup, rover_poss = [], [], []
# main loop
prev_input = None
total_moves = 0
MAX_MOVES = 25
for i in range(steps):
total_moves += 1
start = time.perf_counter()
cur_input = observation
x = cur_input.astype(
np.float).ravel() if prev_input is not None else np.zeros(70)
x = x[10:80] if prev_input is not None else x
x = np.array([x[i] for i in range(len(x)) if not (i % 10 == 0)])
x = np.array([x[i] for i in range(len(x)) if not ((i - 8) % 9 == 0)])
x, rov_pos = get_rover_pos(x, r_tup, e_tup, rover_poss)
x = np.array(x)
rover_poss.append(rov_pos)
"""
x = x[x != 0]
if(len(x) == 1):
x = np.zeros(4)
x = x.tolist()
x.append(-7.)
x = np.array(x)
"""
#print_map(x)
x_t = pre_proc_features.fit_transform(x.reshape(-1, 1))
x_t = x_t.reshape(1, INPUT_SIZE)[0]
print("Shape = ", x_t.shape)
prev_input = cur_input
# forward the policy network and sample action according to the proba distribution
#print_map(x)
proba = model.predict(np.expand_dims(x_t, axis=1).T)
end = time.perf_counter()
action = proba[0].argmax()
print("Time taken = ", end - start)
#run one step
state_temp, reward, done, r_tup, e_tup = my_sim.step(action)
observation = my_sim.state_to_tensor(state_temp)
my_sim.render()
time.sleep(1)
if total_moves == MAX_MOVES:
total_moves = 0
done = True
# if episode is over, reset to beginning
if done:
state_temp = my_sim.reset()
observation = my_sim.state_to_tensor(state_temp)
my_sim.render()
rover_poss = []
action = mprob.argmax() if select == MODEL else rprob.argmax()
y = mprob if select == MODEL else rprob
# categorical action labels
y_data = [0.0] * 4
y_data[action] = 1.0
x_train.append(x_t)
y_train.append(y_data)
y_pred.append(y[action])
print("Action = {0}; model = {1}; ".format(
action, "DL" if select == MODEL else "RND"))
# do one step in our environment
state_temp, reward, done, r_tup, e_tup = my_sim.step(action)
observation = my_sim.state_to_tensor(state_temp)
my_sim.render()
rewards.append(float(reward))
reward_sum += float(reward)
if (reward_sum < MAX_NEG_REWARD):
done = True
# end of an episode
if done:
if (reward_sum > 0):
print(
'At the end of episode {1} the total reward was : {1}'.format(
episode_nb, reward_sum))
# increment episode number
episode_nb += 1