def run_policy(env, get_action, max_ep_len=None, num_episodes=100, render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
a = get_action(torch.Tensor(o.reshape(1,-1)))[0]
o, r, d, _ = env.step(a.data.numpy()[0])
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d'%(n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env, get_action, max_ep_len=None, num_episodes=10):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
while n < num_episodes:
a = get_action(o)
# print(a)
#加入不确定性
# log_std = -0.5 * np.ones(3, dtype=np.float32)
# log_std = torch.nn.Parameter(torch.as_tensor(log_std))
# std = torch.exp(log_std)
# pi = Normal(torch.as_tensor(a), std)
# a = pi.sample()
# a = a.numpy()
# print(" ", a)
o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
if ep_ret == 10:
print("Success!")
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env, get_action, max_ep_len=None, num_episodes=100, render=True, sleep=1e-3,
log=True, verbose=True, reset_state=None, q_action=None, action_parameters=None, random=False):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
if log:
logger = EpochLogger()
r, d, ep_ret, ep_len, n = 0, False, 0, 0, 0
o = env.reset() if reset_state is None else set_state(reset_state, env)
action_parameters = {} if action_parameters is None else action_parameters
while n < num_episodes:
img = None
if render:
img = env.render(mode='rgb_array')
time.sleep(sleep)
if ep_len == 0 and q_action is not None:
a = q_action
elif random:
a = env.action_space.sample()
else:
a = get_action(o, **action_parameters)
o_prev = o
o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
results = {'img': img, 'a': a, 'r': r, 'd': d, 'score': ep_ret, 't': ep_len, 'o': o_prev}
yield results
if d or (ep_len == max_ep_len):
if log:
logger.store(EpRet=ep_ret, EpLen=ep_len)
if verbose:
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
r, d, ep_ret, ep_len, n = 0, False, 0, 0, 0
o = env.reset() if reset_state is None else set_state(reset_state, env)
n += 1
if log:
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
def unscale_action(action_space, scaled_action):
"""
Rescale the action from [-1, 1] to [low, high]
(no need for symmetric action space)
:param action_space: (gym.spaces.box.Box)
:param scaled_action: (np.ndarray)
:return: (np.ndarray)
"""
low, high = action_space.low, action_space.high
return low + (0.5 * (scaled_action + 1.0) * (high - low))
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
a = get_action(o)
unscaled_action = unscale_action(env.action_space, a)
o, r, d, _ = env.step(unscaled_action)
#time.sleep(0.1)
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env, get_action, max_ep_len=None, num_episodes=100, render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
goal_env = hasattr(env, 'goal')
def cat_obs(o):
return np.concatenate([o['observation'], o['desired_goal']], axis=-1)
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
if goal_env:
o = cat_obs(o)
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
a = get_action(o)
o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
if goal_env:
o = cat_obs(o)
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d'%(n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
if goal_env:
o = cat_obs(o)
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
all_feats = []
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
while n < num_episodes:
if render:
env.render(episode=n)
time.sleep(1e-3)
a = get_action(o)
o, r, d, info = env.step(a)
ep_ret += r
ep_len += 1
if "all_feats" in info.keys():
all_feats.append(info["all_feats"])
if d or (ep_len == max_ep_len):
print(f"Coeff: {o[-env.coeff_dim:]}")
print(f"All feats", np.array(all_feats).sum(axis=0))
# import pdb; pdb.set_trace()
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
all_feats = []
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env, policy, max_ep_len=None, num_episodes=100, render=True):
logger = EpochLogger()
obs, reward, done, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
action = policy(obs)
obs, reward, done, _ = env.step(action)
ep_ret += reward
ep_len += 1
if done or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
obs, reward, done, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True,
gamma=1,
key='danger'):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n, ep_info = env.reset(), 0, False, 0, 0, 0, 0
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
a = get_action(o)
o, r, d, info = env.step(a)
ep_info = max(ep_info, info[key])
ep_ret += r * gamma**ep_len
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len, perf=ep_ret, fail=ep_info)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len, ep_info = env.reset(), 0, False, 0, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.log_tabular('perf', average_only=True)
logger.log_tabular('fail', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=1000,
out_name="",
render=False):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
num_episodes = 1000
results = {}
while n < num_episodes:
a = get_action(o)
o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
results[n] = ep_len
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
with open("outputs/" + out_name + ".json", 'w') as f:
json.dump(results, f)
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_adversarial_policy(env,
ego_action,
opp_action,
env_init,
ego_agent,
opp_agent,
max_ep_len=None,
num_episodes=100,
render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
r, d, ep_ret, ep_len, n = 0, False, 0, 0, 0
init_positions = np.random.random_integers(0, 1)
o = env.reset({
'x': env_init['initial_x'][init_positions],
'y': env_init['initial_y'],
'theta': env_init['initial_theta']
})
while n < num_episodes:
if render == True:
env.render()
# time.sleep(1e-3)
#Convert o to RL obs
RLobs = ego_agent.process_obs(o)
Oppobs = opp_agent.process_obs(o)
# Take deterministic actions at test time
a = ego_action(RLobs,
action_mask=ego_agent.aval_paths,
deterministic=True)
ego_speed, ego_steer, a = ego_agent.plan(o, a)
#Opponent decision
a_opp = opp_action(Oppobs,
action_mask=opp_agent.aval_paths,
deterministic=True)
opp_speed, opp_steer, _ = opp_agent.plan(o, a_opp)
action = {
'ego_idx': 0,
'speed': [ego_speed, opp_speed],
'steer': [ego_steer, opp_steer]
}
o, r, d, _ = env.step(action)
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
init_positions = np.random.random_integers(0, 1)
o, r, d, ep_ret, ep_len = env.reset({
'x':
env_init['initial_x'][init_positions],
'y':
env_init['initial_y'],
'theta':
env_init['initial_theta']
}), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env, get_action, max_ep_len=None, num_episodes=100, render=True, env_name=None):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
num_violations = 0
num_target = 0
violations = []
target = []
hit_by_opponent = 0
score_hit = 0
avoid_opponent = 0
hit_feat_counts = []
score_feat_counts = []
avoid_feat_counts = []
episode_feat_counts = []
pellet_counts = 0
power_pellet_counts = 0
eat_ghost_counts = 0
eat_cherry_counts = 0
hit_ghost_counts = 0
pellet_feat_counts = []
power_feat_counts = []
eat_ghost_feat_counts = []
eat_cherry_feat_counts = []
hit_ghost_feat_counts = []
ep_scores = []
demo_obs = []
demo_acs = []
prev_ale = 3
curr_ale = 3
while n < num_episodes:
#hit_by_opponent = 0
#score_hit = 0
#avoid_opponent = 0
if render:
env.render()
time.sleep(1e-3)
a = get_action(o)
o, r, d, info = env.step(a)
#print(r)
if env_name == 'Boxing-ram-v0':
if r == 0:
avoid_opponent += 1
elif r < 0:
hit_by_opponent -= int(r)
else:
score_hit += int(r)
if env_name == 'MsPacman-ram-v0':
curr_ale = env.ale.lives()
if r == 10:
pellet_counts += 1
if r == 50:
power_pellet_counts += 1
if r == 200 or r == 400 or r == 800 or r == 1600:
eat_ghost_counts += 1
if r == 100:
eat_cherry_counts += 1
else:
if curr_ale == prev_ale-1:
hit_ghost_counts += 1
prev_ale = curr_ale
if env_name == 'reacher':
if info['constraint']:
num_violations += 1
if env.get_features()[0]:
num_target += 1
ep_ret += r
ep_len += 1
demo_obs.append(o)
demo_acs.append(a)
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d'%(n, ep_ret, ep_len))
if env_name == 'reacher':
print('Violations %d, Target %d'%(num_violations,num_target))
if env_name == 'Boxing-ram-v0':
print("damage %d"%hit_by_opponent)
print("scores %d"%score_hit)
print("avoid %d"%avoid_opponent)
if env_name == 'MsPacman-ram-v0':
print("pellet %d"%pellet_counts)
print("power pellet %d"%power_pellet_counts)
print("ghosts eaten %d"%eat_ghost_counts)
print("cherry %d"%eat_cherry_counts)
print("hit ghost %d"%hit_ghost_counts)
if env_name == 'Boxing-ram-v0':
episode_feat_counts.append([hit_by_opponent, score_hit, avoid_opponent, ep_ret])
if env_name == 'MsPacman-ram-v0':
episode_feat_counts.append([pellet_counts, power_pellet_counts, eat_ghost_counts, eat_cherry_counts, hit_ghost_counts])
ep_scores.append(ep_ret)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
violations.append(num_violations)
target.append(num_target)
hit_feat_counts.append(hit_by_opponent)
score_feat_counts.append(score_hit)
avoid_feat_counts.append(avoid_opponent)
num_violations = 0
num_target = 0
hit_by_opponent = 0
avoid_opponent = 0
score_hit = 0
pellet_counts = 0
power_pellet_counts = 0
eat_ghost_counts = 0
eat_cherry_counts = 0
hit_ghost_counts = 0
prev_ale = 3
n += 1
if args.env_name == 'reacher':
print(violations)
print(target)
if args.env_name == 'Boxing-ram-v0':
features = {'Features': episode_feat_counts, "Obs": demo_obs, "Scores":ep_scores, "Acs": demo_acs}
pickle.dump(features, open('boxing_demos.pkl', 'wb'))
if args.env_name == 'MsPacman-ram-v0':
features = {'Features': episode_feat_counts,"Scores": ep_scores, "Obs": demo_obs, "Acs":demo_acs}
pickle.dump(features, open('pacman_demos.pkl', 'wb'))
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=False,
params={},
verbose=False):
from upn.visualize.render import forward_env
from numpngw import write_apng
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
test_envs, test_env_names = [], params["test_env_names"][0]
for name in test_env_names:
test_envs.append(gym.make(name))
logger = EpochLogger()
for env_name, env in zip(test_env_names, test_envs):
all_feats = []
all_rews = []
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
coeff = o[-env.coeff_dim:]
acs = []
pbar = tqdm(total=num_episodes)
while n < num_episodes:
#import pdb; pdb.set_trace()
if render:
env.render()
time.sleep(1e-3)
# import pdb; pdb.set_trace()
a = get_action(o)
acs.append(a)
o, r, d, info = env.step(a)
ep_ret += r
ep_len += 1
if "all_feats" in info.keys():
all_feats.append(info["all_feats"])
if d or (ep_len == max_ep_len):
if verbose:
print(f"Coeff: {coeff}")
print(f"All feats", np.array(all_feats).sum(axis=0))
# import pdb; pdb.set_trace()
logger.store(**{f"{env_name}_EpRet": ep_ret})
logger.store(**{f"{env_name}_EpLen": ep_len})
# logger.store(EpRet=ep_ret, EpLen=ep_len)
all_rews.append(ep_ret)
if verbose:
print('Episode %d \t EpRet %.3f \t EpLen %d' %
(n, ep_ret, ep_len))
print(f"{env_name}: reward {ep_ret:.03f}")
if render:
frames = forward_env(env,
np.array(acs),
batch=False,
subrender=False,
resize=0.4)
fps = 10
fname = f"{env_name}_{n:02d}_rew_{ep_ret:.03f}.png"
#os.makedirs(osp.dirname(fname), exist_ok=True)
write_apng(os.path.join(args.folder, fname),
frames,
delay=1000 / fps)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
o = env.reset()
all_feats = []
acs = []
n += 1
pbar.update(1)
print(f"{env_name}: mean reward {np.mean(all_rews):.03f}")
pbar.close()
logger.log_tabular(f'{env_name}_EpRet', with_min_and_max=True)
logger.log_tabular(f'{env_name}_EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True,
seed=None):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
success_num = 0
Handlog = {'maxVelocity': np.array([]), 'maxTorque': np.array([])}
CMAESlog = {'maxVelocity': np.array([]), 'maxTorque': np.array([])}
DRLlog = {
'maxVelocity': np.array([]),
'maxTorque': np.array([]),
'successNum': 0
}
tmpMaxVelocity = np.array([])
tmpMaxTorque = np.array([])
env.__init__("GUI", seed=seed)
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
a = get_action(o)
while n < num_episodes:
if render:
env.render()
time.sleep(1e-3)
a = get_action(o)
for i in range(25):
o, r, d, o_dict = env.step(a)
time.sleep(1 / SIMULATIONFREQUENCY)
tmpMaxTorque = np.append(tmpMaxTorque, np.abs(o_dict['torque']).max())
tmpMaxVelocity = np.append(tmpMaxVelocity,
np.abs(o_dict['velocity']).max())
# time.sleep(1/SIMULATIONFREQUENCY)
# if env.t >2:
# input("hhh")
ep_ret += r
ep_len += 1
# d = False
# if d or (ep_len == max_ep_len):
if ep_len == max_ep_len:
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
satisfy = d
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
# satisfy = input("Is it satisfying? y or n:\n")
if satisfy:
print("done!")
success_num += 1
if len(DRLlog['maxVelocity']) != 0:
DRLlog['maxVelocity'] += tmpMaxVelocity
DRLlog['maxVelocity'] /= success_num
DRLlog['maxTorque'] += tmpMaxTorque
DRLlog['maxTorque'] /= success_num
else:
DRLlog['maxVelocity'] = tmpMaxVelocity
DRLlog['maxTorque'] = tmpMaxTorque
# tmpMaxVelocity, tmpMaxTorque, success = run_Hand(env)
# if len(Handlog['maxVelocity']) != 0:
# Handlog['maxVelocity'] += tmpMaxVelocity
# Handlog['maxVelocity'] /= success_num
# Handlog['maxTorque'] += tmpMaxTorque
# Handlog['maxTorque'] /= success_num
# else:
# Handlog['maxVelocity'] = tmpMaxVelocity
# Handlog['maxTorque'] = tmpMaxTorque
# tmpMaxVelocity, tmpMaxTorque = run_CMAES()
# if len(Handlog['maxVelocity']) != 0:
# CMAESlog['maxVelocity'] += tmpMaxVelocity
# CMAESlog['maxVelocity'] /= success_num
# CMAESlog['maxTorque'] += tmpMaxTorque
# CMAESlog['maxTorque'] /= success_num
# else:
# CMAESlog['maxVelocity'] = tmpMaxVelocity
# CMAESlog['maxTorque'] = tmpMaxTorque
tmpMaxVelocity = np.array([])
tmpMaxTorque = np.array([])
n += 1
DRLlog['successNum'] = success_num
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
return DRLlog, Handlog, CMAESlog
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True,
make_gif=True):
#env = gym.make('flowers-Walker-continuous-v0')
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
logger = EpochLogger()
env_babbling = "random"
norm_obs = False
def get_mu_sigma(v_min, v_max): # assumes sigma has same bounds as mu
random_2dparams = np.random.uniform(v_min, v_max, 2)
return random_2dparams.tolist() # returning mu and sigma
def set_test_env_params(**kwargs):
# if kwargs['stump_height'] is not None:
# random_stump_h = get_mu_sigma(kwargs['stump_height'][0], kwargs['stump_height'][1])
# random_stump_h[1] = 0.1
if 'poly_shape' not in kwargs.keys():
kwargs['poly_shape'] = None
random_stump_h = None
random_tunnel_h = None
random_stump_r = None
random_stump_w = None
random_ob_spacing = None
random_stump_seq = None
if kwargs['stump_height'] is not None:
random_stump_h = [kwargs['stump_height'], 0.1]
if 'stump_rot' in kwargs.keys() and kwargs['stump_rot'] is not None:
random_stump_r = [kwargs['stump_rot'], 0.1]
if kwargs['stump_width'] is not None:
random_stump_w = [kwargs['stump_width'], 0.1]
if kwargs['tunnel_height'] is not None:
random_tunnel_h = [kwargs['tunnel_height'], 0.1]
if kwargs['obstacle_spacing'] is not None:
random_ob_spacing = kwargs['obstacle_spacing']
if kwargs['stump_seq'] is not None:
random_stump_seq = kwargs['stump_seq']
env.env.set_environment(roughness=kwargs['roughness'],
stump_height=random_stump_h,
stump_width=random_stump_w,
stump_rot=random_stump_r,
tunnel_height=None,
obstacle_spacing=random_ob_spacing,
gap_width=kwargs['gap_width'],
step_height=kwargs['step_height'],
step_number=kwargs['step_number'],
poly_shape=kwargs['poly_shape'],
stump_seq=random_stump_seq)
def poly_2_width_height(params):
scaling = 14 / 30.0
obstacle_polygon = [(-0.5, 0), (-0.5, 0.25), (-0.25, 0.5), (0.25, 0.5),
(0.5, 0.25), (0.5, 0)]
paired_params = [[params[i], params[i + 1]]
for i in range(0, len(params), 2)]
# first recover polygon coordinate
poly_coord = []
for i, (b, d) in enumerate(zip(obstacle_polygon, paired_params)):
# print(paired_params)
if i != 0 and i != (len(obstacle_polygon) - 1):
poly_coord.append([(b[0] * scaling) + (d[0] * scaling),
(b[1] * scaling) + (d[1] * scaling)])
else:
poly_coord.append([(b[0] * scaling) + (d[0] * scaling),
(b[1] * scaling)])
# the find maximal width and height
poly_coord = np.array(poly_coord)
min_x = np.min(poly_coord[:, 0])
max_x = np.max(poly_coord[:, 0])
min_y = np.min(poly_coord[:, 1])
max_y = np.max(poly_coord[:, 1])
height_width_params = [(max_x - min_x) / scaling,
(max_y - min_y) / scaling]
return np.round(height_width_params, 2)
# simple exp: random short fails compared to gmm -> [0.84,5.39] run 11
env_kwargs = {
'roughness': None,
'stump_height':
[0.50, 0.50], #stump_levels = [[0., 0.66], [0.66, 1.33], [1.33, 2.]]
'tunnel_height': None,
'stump_rot': None,
'stump_width': None,
'obstacle_spacing': 4,
'gap_width': None,
'step_height': None,
'step_number': None
}
#test_env_list = pickle.load(open("/home/remy/projects/spinningup/teachers/test_sets/poly_shape0_4.0.pkl", "rb"))
test_env_list = pickle.load(
open(
"/home/remy/projects/spinningup/teachers/test_sets/stump_height0_3.0obstacle_spacing0_6.0.pkl",
"rb"))
test_env_list = pickle.load(
open(
"/home/remy/projects/spinningup/teachers/test_sets/stump_seq0_6.0.pkl",
"rb"))
#test_env_list = params_2_env_list([[0.4,0.8]],['stump_height','obstacle_spacing']) #short agent seed 7(or 11)
#test_env_list = params_2_env_list([[0,0],[0.7,1.0],[1.6,5.5],[1.9,0.01]],['stump_height', 'obstacle_spacing']) # default agent seed 0
#test_env_list = params_2_env_list([[0,0],[3.0,0.0],[3.0,5], [1.5,0.5]],['stump_height', 'obstacle_spacing']) # long agent seed 0
#test_env_list = params_2_env_list([[5.0,1.0,5.0,1.0,5.0,1.0,5.0,1.0,5.0,1.0]],'stump_seq') # long agent seed 0
# final_list = []
# for i in [19]:
# final_list.append(test_env_list[i])
# for i in range(5):
# prev_args = copy.copy(final_list[-1])
# last_poly = prev_args['poly_shape']
# prev_args['poly_shape'] = np.clip(np.random.normal(last_poly,0.5),0,10)
# final_list.append(prev_args)
# test_env_list = final_list
# #print(test_env_list)
if norm_obs:
norm = MaxMinFilter(env_params_dict=env_kwargs)
# increments = np.array([-0.4, 0, -0.4, 0.2, -0.2, 0.4, 0.2, 0.4, 0.4, 0.2, 0.4, 0.0])
# init_poly = np.zeros(12)
# init_poly += 5
for i, args in enumerate(test_env_list):
#args = params_2_env_list([init_poly],'poly_shape')[0]
# if i not in [0,1,3,6,4]:
# continue
#if i not in [1,5,8,10,25,35]:
# continue
#print("{}: {}".format(i, args['poly_shape']))
set_test_env_params(**args)
#init_poly += increments
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
img = env.render(mode='rgb_array')
o = norm(o) if norm_obs else o
obss = [o]
skip = 2
cpt = 0
#wh = poly_2_width_height(args['poly_shape'])
save_img = False
images = []
while n < num_episodes:
if render:
cpt += 1
if (cpt % skip) == 0:
if make_gif:
img = env.render(mode='rgb_array')
images.append(img)
if save_img:
plt.imsave(
"graphics/walker_images/a_quadru_complex_walker_gmm_{}_{}_{}.png"
.format(wh, i, cpt),
np.array(img)[150:315, :-320, :])
else:
env.render()
time.sleep(1e-3)
a = get_action(o)
o, r, d, _ = env.step(a)
o = norm(o) if norm_obs else o
obss.append(o)
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
#print('Episode {}:{} \t EpRet {} \t EpLen {}'.format(i, wh, ep_ret, ep_len))
print('Episode {}:{} \t EpRet {} \t EpLen {}'.format(
i, args['stump_height'], ep_ret, ep_len))
#set_test_env_params(**env_kwargs)
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
o = norm(o) if norm_obs else o
n += 1
#print("MAX:{}".format(np.max(obss, axis=0)))
#print("MIN:{}".format(np.min(obss,axis=0)))
#
# logger.log_tabular('EpRet', with_min_and_max=True)
# logger.log_tabular('EpLen', average_only=True)
# logger.dump_tabular()
# print(len(images))
# print(np.array(images[0]).shape)
#[150:315,:-320,:] for long
#[200:315,:-320,:] for default
imageio.mimsave(
'graphics/demo_quadru_seq_env_{}.gif'.format(i),
[np.array(img)[110:315, :-320, :] for i, img in enumerate(images)],
fps=29)
def run_policy(env,
get_action,
max_ep_len=None,
num_episodes=100,
render=True,
try_rollouts=0,
steps_per_try_rollout=0):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
torch.manual_seed(3)
np.random.seed(3)
random.seed(3)
logger = EpochLogger()
o, r, done, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
rollout = []
while n < num_episodes:
if try_rollouts != 0:
if not rollout:
rollout = do_rollouts(get_action,
env,
o,
steps_per_try_rollout,
try_rollouts,
is_eval=True,
take_worst_rollout=False)
a, v, logp, _o, _r, _done, _info = rollout.pop(0)
o, r, done, info = env.step(a)
assert np.array_equal(o, _o)
assert r == _r
assert done == _done
step_output = o, r, done, info
else:
a = get_action(o)[0]
step_output = env.step(a)
if render:
env.render()
# time.sleep(1e-3)
if hasattr(env, 'last_step_output'):
step_output = env.last_step_output
o, r, done, info = step_output
ep_ret += r
ep_len += 1
if done or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' % (n, ep_ret, ep_len))
o, r, done, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
def run_policy(env,
get_action,
save_dir,
max_ep_len=10000,
num_episodes=10,
render=True):
assert env is not None, \
"Environment not found!\n\n It looks like the environment wasn't saved, " + \
"and we can't run the agent in it. :( \n\n Check out the readthedocs " + \
"page on Experiment Outputs for how to handle this situation."
dir_name = 'trajectory{}st_{}episode'.format(sample_step_per_trj,
num_episodes) #!dirname
dir_path = osp.join(save_dir, dir_name)
os.makedirs(dir_path) #, exist_ok=True) #すでに存在する場合
if save_movie:
env = gym.wrappers.Monitor(env,
dir_path + '/movies',
video_callable=(lambda n: n < 10))
logger = EpochLogger()
o, r, d, ep_ret, ep_len, n = env.reset(), 0, False, 0, 0, 0
observations = []
actions = []
results = []
while n < num_episodes:
for t in range(max_ep_len):
if render:
env.render()
time.sleep(1e-5) #1e-2
a = get_action(o)
if t < sample_step_per_trj:
observations.append(o)
actions.append(a)
o, r, d, _ = env.step(a)
ep_ret += r
ep_len += 1
if d or (ep_len == max_ep_len):
logger.store(EpRet=ep_ret, EpLen=ep_len)
print('Episode %d \t EpRet %.3f \t EpLen %d' %
(n, ep_ret, ep_len))
results.append([n, ep_ret, ep_len])
o, r, d, ep_ret, ep_len = env.reset(), 0, False, 0, 0
n += 1
break
logger.log_tabular('EpRet', with_min_and_max=True)
logger.log_tabular('EpLen', average_only=True)
logger.dump_tabular()
#save csv
df_obs = pd.DataFrame(observations)
df_act = pd.DataFrame(actions)
df_results = pd.DataFrame(
results,
columns=['Episode', 'EpRet', 'Eplen'],
)
#sample_r_mean= df_results['EpRet'].mean()
df_obs.to_csv(osp.join(dir_path, "observations.csv"),
sep=",",
header=False,
index=False)
df_act.to_csv(osp.join(dir_path, "actions.csv"),
sep=",",
header=False,
index=False)
df_results.to_csv(osp.join(dir_path, "each_results.csv"),
sep=",",
index=False)
df_results.describe().to_csv(osp.join(dir_path, "results_describe.csv"),
sep=",")
