def log_probs(self, states, actions):
ts_ = trajectories.TimeStep(
tf.stack([trajectories.StepType.MID] * 256, 0),
tf.constant([1.] * 256, dtype=tf.float32),
tf.constant([1.] * 256, dtype=tf.float32),
tf.cast(states, tf.float32))
dist = self.policy.distribution(ts_)
return dist.action.log_prob(actions)
def act(self, states):
"""Act from states.
Args:
states: batch of states
Returns:
actions
"""
ts = trajectories.TimeStep(trajectories.StepType.MID,
tf.constant(1, dtype=tf.float32),
tf.constant(1, dtype=tf.float32),
tf.cast(states[0], tf.float32))
ts2 = trajectories.TimeStep(
tf.expand_dims(trajectories.StepType.MID, 0),
tf.constant([1], dtype=tf.float32),
tf.constant([1], dtype=tf.float32), tf.cast(states, tf.float32))
act_d = self.policy.action(ts)
action = tf.constant(act_d.action.item())
log_prob = self.policy._policy.distribution(ts2).action.log_prob( # pylint: disable=protected-access
act_d.action)
return action, log_prob
def main(_):
if FLAGS.eager:
tf.config.experimental_run_functions_eagerly(FLAGS.eager)
tf.random.set_seed(FLAGS.seed)
# np.random.seed(FLAGS.seed)
# random.seed(FLAGS.seed)
print('Env name: %s'%FLAGS.env_name)
if 'procgen' in FLAGS.env_name:
_, env_name, train_levels, _ = FLAGS.env_name.split('-')
env = procgen_wrappers.TFAgentsParallelProcGenEnv(
1,
normalize_rewards=True,
env_name=env_name,
num_levels=int(train_levels),
start_level=0)
state_env = None
timestep_spec = trajectories.time_step_spec(
observation_spec=specs.ArraySpec(env._observation_spec.shape, np.uint8), # pylint: disable=protected-access
reward_spec=specs.ArraySpec(shape=(), dtype=np.float32))
data_spec = trajectory.from_transition(
timestep_spec,
policy_step.PolicyStep(
action=env._action_spec, # pylint: disable=protected-access
info=specs.ArraySpec(shape=(), dtype=np.int32)), timestep_spec)
n_state = None
# ckpt_steps = [10_000_000,15_000_000,20_000_000,25_000_000]
ckpt_steps = [25_000_000]
elif FLAGS.env_name.startswith('pixels-dm'):
if 'distractor' in FLAGS.env_name:
_, _, domain_name, _, _ = FLAGS.env_name.split('-')
else:
_, _, domain_name, _ = FLAGS.env_name.split('-')
if domain_name in ['cartpole']:
FLAGS.set_default('action_repeat', 8)
elif domain_name in ['reacher', 'cheetah', 'ball_in_cup', 'hopper']:
FLAGS.set_default('action_repeat', 4)
elif domain_name in ['finger', 'walker']:
FLAGS.set_default('action_repeat', 2)
env, state_env = utils.load_env(FLAGS.env_name, FLAGS.seed,
FLAGS.action_repeat, FLAGS.frame_stack,
FLAGS.obs_type)
if FLAGS.obs_type == 'pixels':
data_spec = trajectory.from_transition(
env.time_step_spec(), policy_step.PolicyStep(env.action_spec()),
env.time_step_spec())
ckpt_steps = FLAGS.ckpt_timesteps[0]
else:
data_spec = trajectory.from_transition(
state_env.time_step_spec(),
policy_step.PolicyStep(state_env.action_spec()),
state_env.time_step_spec())
n_state = state_env.observation_spec().shape[0]
ckpt_steps = FLAGS.ckpt_timesteps[0]
if FLAGS.numpy_dataset:
tf.io.gfile.makedirs(os.path.join(FLAGS.save_dir, 'datasets'))
def shard_fn(shard):
return os.path.join(
FLAGS.save_dir, 'datasets', FLAGS.env_name + '__%d__%d__%d.npy' %
(int(ckpt_steps[-1]), FLAGS.max_timesteps, shard))
observer = tf_utils.NumpyObserver(shard_fn, env)
observer.allocate_arrays(FLAGS.max_timesteps)
else:
shard_fn = os.path.join(
FLAGS.save_dir, 'datasets',
FLAGS.env_name + '__%d__%d.tfrecord.shard-%d-of-%d' %
(int(ckpt_steps[-1]), FLAGS.max_timesteps, FLAGS.worker_id,
FLAGS.total_workers))
observer = DummyObserver(
shard_fn, data_spec, py_mode=True, compress_image=True)
def load_model(checkpoint):
checkpoint = int(checkpoint)
print(checkpoint)
if FLAGS.env_name.startswith('procgen'):
env_id = [i for i, name in enumerate(
PROCGEN_ENVS) if name == env_name][0]+1
if checkpoint == 10_000_000:
ckpt_iter = '0000020480'
elif checkpoint == 15_000_000:
ckpt_iter = '0000030720'
elif checkpoint == 20_000_000:
ckpt_iter = '0000040960'
elif checkpoint == 25_000_000:
ckpt_iter = '0000051200'
policy_weights_dir = ('ppo_darts/'
'2021-06-22-16-36-54/%d/policies/checkpoints/'
'policy_checkpoint_%s/' % (env_id, ckpt_iter))
policy_def_dir = ('ppo_darts/'
'2021-06-22-16-36-54/%d/policies/policy/' % (env_id))
model = py_tf_eager_policy.SavedModelPyTFEagerPolicy(
policy_def_dir,
time_step_spec=env._time_step_spec, # pylint: disable=protected-access
action_spec=env._action_spec, # pylint: disable=protected-access
policy_state_spec=env._observation_spec, # pylint: disable=protected-access
info_spec=tf.TensorSpec(shape=(None,)),
load_specs_from_pbtxt=False)
model.update_from_checkpoint(policy_weights_dir)
model.actor = model.action
else:
if 'ddpg' in FLAGS.algo_name:
model = ddpg.DDPG(
env.observation_spec(),
env.action_spec(),
cross_norm='crossnorm' in FLAGS.algo_name)
elif 'crr' in FLAGS.algo_name:
model = awr.AWR(
env.observation_spec(),
env.action_spec(), f='bin_max')
elif 'awr' in FLAGS.algo_name:
model = awr.AWR(
env.observation_spec(),
env.action_spec(), f='exp_mean')
elif 'sac_v1' in FLAGS.algo_name:
model = sac_v1.SAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'asac' in FLAGS.algo_name:
model = asac.ASAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
elif 'sac' in FLAGS.algo_name:
model = sac.SAC(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0],
cross_norm='crossnorm' in FLAGS.algo_name,
pcl_actor_update='pc' in FLAGS.algo_name)
elif 'pcl' in FLAGS.algo_name:
model = pcl.PCL(
env.observation_spec(),
env.action_spec(),
target_entropy=-env.action_spec().shape[0])
if 'distractor' in FLAGS.env_name:
ckpt_path = os.path.join(
('experiments/'
'20210622_2023.policy_weights_sac_1M_dmc_distractor_hard_pixel/'),
'results', FLAGS.env_name+'__'+str(checkpoint))
else:
ckpt_path = os.path.join(
('experiments/'
'20210607_2023.policy_weights_dmc_1M_SAC_pixel'), 'results',
FLAGS.env_name + '__' + str(checkpoint))
model.load_weights(ckpt_path)
print('Loaded model weights')
return model
# previous_time = time.time()
timestep = env.reset()
episode_return = 0
episode_timesteps = 0
actions = []
time_steps = []
def get_state_or_pixels(obs, obs_type):
# obs of shape 1 x 84 x 84 x (n_state*frame_stack + 3*frame_stack)
if len(obs.shape) == 4:
obs = obs[0]
if obs_type == 'state':
obs = obs[0, 0, :n_state]
else:
obs_tmp = []
for i in range(FLAGS.frame_stack):
obs_tmp.append(obs[:, :, (i + 1) * (n_state) +
i * 3:((i + 1) * (n_state) + (i + 1) * 3)])
obs = np.concatenate(obs_tmp, axis=-1)
return obs
k_model = 0
model = load_model(ckpt_steps[k_model])
reload_model = False
def linear_scheduling(t): # pylint: disable=unused-variable
return 0.1 - 3.96e-9* t
mixture_freq = FLAGS.max_timesteps // len(ckpt_steps)
for i in tqdm.tqdm(range(FLAGS.max_timesteps)):
if (i % mixture_freq) == 0 and i > 0:
reload_model = True
if np.all(timestep.is_last()):
if FLAGS.env_name.startswith('procgen'):
timestep = trajectories.TimeStep(
timestep.step_type[0], timestep.reward[0], timestep.discount[0],
(timestep.observation[0] * 255).astype(np.uint8))
time_steps.append(
ts.termination(
get_state_or_pixels(timestep.observation()[0], 'state')
if FLAGS.obs_type == 'state' else timestep.observation,
timestep.reward if timestep.reward is not None else 1.0))
# Write the episode into the TF Record
for l in range(len(time_steps) - 1):
t_ = min(l + FLAGS.n_step_returns, len(time_steps) - 1)
n_step_return = 0.
for j in range(l, t_):
if len(time_steps[j].reward.shape) == 1:
r_t = time_steps[j].reward[0]
else:
r_t = time_steps[j].reward
n_step_return += FLAGS.discount**j * r_t
t_ = min(l + 1 + FLAGS.n_step_returns, len(time_steps) - 1)
n_step_return_tp1 = 0.
for j in range(l + 1, t_):
if len(time_steps[j].reward.shape) == 1:
r_t = time_steps[j].reward[0]
else:
r_t = time_steps[j].reward
n_step_return_tp1 += FLAGS.discount**j * r_t
if len(time_steps[l].observation.shape) == 4:
if len(time_steps[l].reward.shape) == 1:
time_steps[l] = trajectories.TimeStep(time_steps[l].step_type[0],
n_step_return,
time_steps[l].discount[0],
time_steps[l].observation[0])
else:
time_steps[l] = trajectories.TimeStep(time_steps[l].step_type,
n_step_return,
time_steps[l].discount,
time_steps[l].observation[0])
if len(time_steps[l + 1].observation.shape) == 4:
if len(time_steps[l + 1].reward.shape) == 1:
time_steps[l + 1] = trajectories.TimeStep(
time_steps[l + 1].step_type[0], n_step_return_tp1,
time_steps[l + 1].discount[0], time_steps[l + 1].observation[0])
else:
time_steps[l + 1] = trajectories.TimeStep(
time_steps[l + 1].step_type, n_step_return_tp1,
time_steps[l + 1].discount, time_steps[l + 1].observation[0])
traj = trajectory.from_transition(time_steps[l], actions[l],
time_steps[l + 1])
if FLAGS.numpy_dataset:
traj = Traj(traj, next_obs=time_steps[l+1].observation)
observer(traj)
else:
observer(traj)
timestep = env.reset()
print(episode_return)
episode_return = 0
episode_timesteps = 0
# previous_time = time.time()
actions = []
time_steps = []
if reload_model:
k_model += 1
model = load_model(ckpt_steps[k_model])
reload_model = False
if FLAGS.env_name.startswith('procgen'):
timestep = trajectories.TimeStep(
timestep.step_type[0], timestep.reward[0], timestep.discount[0],
(timestep.observation[0] * 255).astype(np.uint8))
if episode_timesteps == 0:
time_steps.append(
ts.restart(
get_state_or_pixels(timestep.observation, 'state') if FLAGS
.obs_type == 'state' else (timestep.observation)))
elif not timestep.is_last():
time_steps.append(
ts.transition(
get_state_or_pixels(timestep.observation[0], 'state')
if FLAGS.obs_type == 'state' else (timestep.observation),
timestep.reward if timestep.reward is not None else 0.0,
timestep.discount))
if FLAGS.env_name.startswith('procgen'):
# eps_t = linear_scheduling(i)
eps_t = 0
u = np.random.uniform(0, 1, size=1)
if u > eps_t:
timestep_act = trajectories.TimeStep(
timestep.step_type, timestep.reward, timestep.discount,
timestep.observation.astype(np.float32) / 255.)
action = model.actor(timestep_act)
action = action.action
else:
action = np.random.choice(
env.action_spec().maximum.item() + 1, size=1)[0]
next_timestep = env.step(action)
info_arr = np.array(env._infos[0]['level_seed'], dtype=np.int32) # pylint: disable=protected-access
actions.append(policy_step.PolicyStep(action=action, state=(),
info=info_arr))
else:
action = model.actor(
tf.expand_dims(
get_state_or_pixels(timestep.observation[0], 'pixel')
if FLAGS.obs_type == 'state' else (timestep.observation[0]), 0),
sample=True)
next_timestep = env.step(action)
actions.append(
policy_step.PolicyStep(action=action.numpy()[0], state=(), info=()))
episode_return += next_timestep.reward[0]
episode_timesteps += 1
timestep = next_timestep
if FLAGS.numpy_dataset:
observer.save(n_shards=10)