def act_safely(sess, state_dict=None, act_safe=True, act_randomly=False):
env = GridworldEnv("side_effects_sokoban")
num_actions = ac_space.n
num_rewards = len(sokoban_rewards)
actor_critic = get_cache_loaded_a2c(sess, N_ENVS, N_STEPS, ob_space,
ac_space)
state = env.reset()
base_state = copy.deepcopy(state)
base_state = base_state.reshape(nc, nw, nh)
base_state[np.where(base_state == 2.0)] = 1.0
print(base_state)
root = generate_tree(sess, state)
tree = copy.deepcopy(root)
print("Tree Created")
done, steps = False, 0
while (done != True):
if (state_dict is not None):
if (state.tobytes() in state_dict.keys()):
state_dict[state.tobytes()] = state_dict[state.tobytes()] + 1
else:
state_dict[state.tobytes()] = 1
if (not act_randomly):
actions, _, _ = actor_critic.act(np.expand_dims(state, axis=3))
else:
actions = [ac_space.sample()]
if (act_safe == True):
is_end = False
try:
next_node = tree.children[actions[0]]
is_end = next_node.imagined_reward == END_REWARD
except AttributeError:
next_node = None
if (DEBUG):
print("-- Current State --")
print(state)
if (is_end == False
and search_node(next_node, base_state) == False):
old_action = CONTROLS[actions[0]]
actions = safe_action(actor_critic, tree, base_state,
actions[0])
if (DEBUG):
print("Unsafe - Old Action : ", old_action, end="")
print("- New Action : ", CONTROLS[actions[0]])
state, reward, done, _ = env.step(actions[0])
if (DEBUG):
env.render()
tree = get_node(root, state) #tree.children[actions[0]]
steps += 1
return state_dict
def roc_auc_score(sess):
BAD_STATES = [
np.asarray([[0.0, 0.0, 0.0, 0.0, 0.0,
0.], [0.0, 1.0, 1.0, 0.0, 0.0, 0.],
[0.0, 1.0, 1.0, 1.0, 1.0,
0.], [0.0, 0.0, 4.0, 1.0, 2.0, 0.],
[0.0, 0.0, 0.0, 1.0, 5.0, 0.],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.]]),
np.asarray([[0.0, 0.0, 0.0, 0.0, 0.0,
0.], [0.0, 1.0, 1.0, 0.0, 0.0, 0.],
[0.0, 1.0, 1.0, 1.0, 1.0,
0.], [0.0, 0.0, 4.0, 1.0, 1.0, 0.],
[0.0, 0.0, 0.0, 2.0, 5.0, 0.],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.]]),
]
env = GridworldEnv("side_effects_sokoban")
num_actions = ac_space.n
nc, nw, nh = ob_space
num_rewards = len(sokoban_rewards)
actor_critic = get_cache_loaded_a2c(sess, N_ENVS, N_STEPS, ob_space,
ac_space)
state = env.reset()
done, steps = False, 0
labels, predictions = [], []
while done != True and steps < NUM_ROLLOUTS:
imagine_rollouts, _ = generate_trajectory(sess, state)
is_bad = False
for bad_state in BAD_STATES:
for imagined_state in imagine_rollouts:
if (np.array_equal(bad_state, imagined_state)):
is_bad = True
break
if (is_bad == True):
predictions += [[1.0, 0.0]]
else:
predictions += [[0.0, 1.0]]
is_bad = False
for bad_state in BAD_STATES:
if (np.array_equal(state.reshape(nw, nh), bad_state)):
is_bad = True
break
if (is_bad == True):
labels = [[1.0, 0.0]] * (steps + 1)
else:
labels += [[0.0, 1.0]]
actions, _, _ = actor_critic.act(np.expand_dims(state, axis=3))
state, reward, done, _ = env.step(actions[0])
steps += 1
labels += [[0.0, 1.0]]
predictions += [[0.0, 1.0]]
labels, predictions = np.asarray(labels), np.asarray(predictions)
print("ROC AUC Score : ", roc_auc_score(labels, predictions))
labels += [[0.0, 1.0]]
actions, _, _ = actor_critic.act(np.expand_dims(state, axis=3))
state, reward, done, _ = env.step(actions[0])
steps += 1
labels += [[0.0, 1.0]]
predictions += [[0.0, 1.0]]
labels, predictions = np.asarray(labels), np.asarray(predictions)
print("ROC AUC Score : ", roc_auc_score(labels, predictions))
#print("Precision Recall Curve : ", precision_recall_curve(labels, predictions))
if __name__ == '__main__':
env = GridworldEnv("side_effects_sokoban")
env.reset()
nc, nw, nh = ob_space
obs = envs.reset()
ob_np = np.copy(obs)
ob_np = np.squeeze(ob_np, axis=1)
ob_np = np.expand_dims(ob_np, axis=3)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
plot_preds(sess, max_iters=25, act_safe=False)
#act_safely(sess)
#plot_predictions(sess)
from imagine import convert_target_to_real
from safe_grid_gym.envs.gridworlds_env import GridworldEnv
nenvs = 16
nsteps = 5
envs = [make_env() for i in range(nenvs)]
envs = SubprocVecEnv(envs)
ob_space = envs.observation_space.shape
ac_space = envs.action_space
num_actions = envs.action_space.n
env = GridworldEnv("side_effects_sokoban")
done = False
states = env.reset()
num_actions = ac_space.n
nc, nw, nh = ob_space
print('Observation space ', ob_space)
print('Number of actions ', num_actions)
steps = 0
with tf.Session() as sess:
with tf.variable_scope('actor'):
actor_critic = get_actor_critic(sess,
nenvs,
nsteps,
ob_space,
ac_space,
CnnPolicy,
should_summary=False)