def test_UniformBatchCollector_OVER_CAPACITY(gym_discrete_setup):
# region ::Type hint bloc ...
exp_spec: ExperimentSpec
playground: GymPlayground
trajectory_collector: TrajectoryCollector
uni_batch_collector: UniformBatchCollector
env: Union[TimeLimit, Any]
# endregion
uni_batch_collector = UniformBatchCollector(capacity=200)
exp_spec, playground, trajectory_collector, _, env, _ = gym_discrete_setup
timestep_patern = [100, 100, 10]
with pytest.raises(AssertionError):
for trj in range(3):
for step in range(timestep_patern[trj]):
obs = np.ones((1, 4))
act = 1
rew = 1
dummy_events = (obs, act, rew)
trajectory_collector.collect_OAR(*dummy_events)
trajectory_collector.trajectory_ended()
trajectory_collector.compute_Qvalues_as_rewardToGo()
aTrajectory = trajectory_collector.pop_trajectory_and_reset()
uni_batch_collector.collect(aTrajectory)
def test_SamplingContainer_DISCRETE_BASIC(gym_discrete_setup):
(exp_spec, playground) = gym_discrete_setup
env = playground.env
# todo: finish implementing test case
""" STEP-1: Container instantiation"""
the_TRAJECTORY_COLLECTOR = TrajectoryCollector(exp_spec, playground)
the_UNI_BATCH_COLLECTOR = UniformBatchCollector(
capacity=exp_spec.batch_size_in_ts)
"""--Simulator code: trajectorie -------------------------------------------------------------------------------"""
while the_UNI_BATCH_COLLECTOR.is_not_full():
observation = env.reset()
step = 0
"""--Simulator code: time-step------------------------------------------------------------------------------"""
while True:
step += 1
# env.render() # (!) keep render() turn OFF during unit test
print(observation)
action = env.action_space.sample(
) # sample a random action from the action space (aka: a random agent)
observation, reward, done, info = env.step(action)
print("\ninfo: {}\n".format(info))
""" STEP-2: append sample to container"""
the_TRAJECTORY_COLLECTOR.collect_OAR(observation, action, reward)
if done:
""" STEP-3: acces container"""
_ = the_TRAJECTORY_COLLECTOR.trajectory_ended()
the_TRAJECTORY_COLLECTOR.compute_Qvalues_as_rewardToGo()
trj_container = the_TRAJECTORY_COLLECTOR.pop_trajectory_and_reset(
)
collected_timestep = len(trj_container)
assert step == collected_timestep, "Trajectory lenght do not match nb collected_timestep"
the_UNI_BATCH_COLLECTOR.collect(trj_container)
np_array_obs, np_array_act, np_array_rew, Q_values, trajectory_return, trajectory_lenght = trj_container.unpack(
)
print(
"\n\n----------------------------------------------------------------------------------------"
"\n Trajectorie finished after {} timesteps".format(step +
1))
print("observation: {}".format(np_array_obs))
print("reward: {}".format(np_array_rew))
break
def _instantiate_data_collector(self):
"""
Data collector utility
:return: Collertor utility
:rtype: (TrajectoryCollector, UniformBatchCollector)
"""
the_TRAJECTORY_COLLECTOR = TrajectoryCollector(self.exp_spec,
self.playground)
the_UNI_BATCH_COLLECTOR = UniformBatchCollector(
self.exp_spec.batch_size_in_ts)
return the_TRAJECTORY_COLLECTOR, the_UNI_BATCH_COLLECTOR
def test_UniformBatchContainer_METRIC(gym_discrete_setup):
# region ::Type hint bloc ...
exp_spec: ExperimentSpec
playground: GymPlayground
trajectory_collector: TrajectoryCollector
uni_batch_collector: UniformBatchCollector
env: Union[TimeLimit, Any]
# endregion
uni_batch_collector = UniformBatchCollector(capacity=200)
exp_spec, playground, trajectory_collector, _, env, _ = gym_discrete_setup
trj = 0
while uni_batch_collector.is_not_full():
trj += 1
for step in range(100):
obs = np.ones((1, 4))
act = 1
rew = 1
dummy_events = (obs, act, rew)
trajectory_collector.collect_OAR(*dummy_events)
trajectory_collector.trajectory_ended()
trajectory_collector.compute_Qvalues_as_rewardToGo()
aTrajectory = trajectory_collector.pop_trajectory_and_reset()
uni_batch_collector.collect(aTrajectory)
aBatch_contaner = uni_batch_collector.pop_batch_and_reset()
batch_average_trjs_return, batch_average_trjs_lenght = aBatch_contaner.get_basic_metric()
assert batch_average_trjs_return == 100
assert batch_average_trjs_lenght == 100
def test_UniformBatchCollector_AT_BATCH_CAPACITY(gym_discrete_setup):
# region ::Type hint bloc ...
exp_spec: ExperimentSpec
playground: GymPlayground
trajectory_collector: TrajectoryCollector
uni_batch_collector: UniformBatchCollector
env: Union[TimeLimit, Any]
# endregion
uni_batch_collector = UniformBatchCollector(capacity=200)
exp_spec, playground, trajectory_collector, _, env, _ = gym_discrete_setup
for trj in range(2):
for step in range(100):
obs = np.ones((1, 4))
act = 1
rew = 1
dummy_events = (obs, act, rew)
trajectory_collector.collect_OAR(*dummy_events)
trajectory_collector.trajectory_ended()
trajectory_collector.compute_Qvalues_as_rewardToGo()
aTrajectory = trajectory_collector.pop_trajectory_and_reset()
uni_batch_collector.collect(aTrajectory)
state = uni_batch_collector.internal_state()
assert len(state.trajectories_list) == 2
assert state.timestep_count == 200
assert state.trajectory_count == 2
assert state.remaining_batch_space == uni_batch_collector.CAPACITY - 200
assert not uni_batch_collector.is_not_full()
def gym_discrete_setup():
exp_spec = ExperimentSpec(batch_size_in_ts=1000,
max_epoch=2,
theta_nn_hidden_layer_topology=(2, 2))
playground = GymPlayground('LunarLander-v2')
trajectory_collector = TrajectoryCollector(exp_spec, playground)
uni_batch_collector = UniformBatchCollector(
capacity=exp_spec.batch_size_in_ts)
env = playground.env
initial_observation = env.reset()
yield exp_spec, playground, trajectory_collector, uni_batch_collector, env, initial_observation
def train(env_name='CartPole-v0',
hidden_sizes=[32],
lr=1e-2,
epochs=50,
batch_size=5000,
render=False):
# make environment, check spaces, get obs / act dims
# env = gym.make(env_name) # ////// Original bloc //////
REINFORCE_integration_test = { # \\\\\\ My bloc \\\\\\
'prefered_environment': env_name,
'paramameter_set_name': 'REINFORCE integration test on CartPole-v0',
'batch_size_in_ts': batch_size,
'max_epoch': epochs,
'discounted_reward_to_go': False,
'discout_factor': 0.999,
'learning_rate': lr,
'theta_nn_h_layer_topo': tuple(hidden_sizes),
'random_seed': 42,
'theta_hidden_layers_activation': tf.nn.tanh, # tf.nn.relu,
'theta_output_layers_activation': None,
'render_env_every_What_epoch': 100,
'print_metric_every_what_epoch': 5,
}
playground = BLOC.GymPlayground(env_name) # \\\\\\ My bloc \\\\\\
env = playground.env # \\\\\\ My bloc \\\\\\
exp_spec = BLOC.ExperimentSpec() # \\\\\\ My bloc \\\\\\
exp_spec.set_experiment_spec(
REINFORCE_integration_test) # \\\\\\ My bloc \\\\\\
consol_print_learning_stats = ConsolPrintLearningStats( # \\\\\\ My bloc \\\\\\
exp_spec,
exp_spec.print_metric_every_what_epoch) # \\\\\\ My bloc \\\\\\
assert isinstance(env.observation_space, Box), \
"This example only works for envs with continuous state spaces."
assert isinstance(env.action_space, Discrete), \
"This example only works for envs with discrete action spaces."
obs_dim = env.observation_space.shape[0]
n_acts = env.action_space.n
# make core of policy network
# obs_ph = tf.placeholder(shape=(None, obs_dim), dtype=tf.float32) # ////// Original bloc //////
obs_ph, act_ph, weights_ph = BLOC.gym_playground_to_tensorflow_graph_adapter(
playground) # \\\\\\ My bloc \\\\\\
# logits = mlp(obs_ph, sizes=hidden_sizes+[n_acts]) # ////// Original bloc //////
# logits = BLOC.build_MLP_computation_graph(obs_ph, playground, # \\\\\\ My bloc \\\\\\
# hidden_layer_topology=tuple(hidden_sizes)) # \\\\\\ My bloc \\\\\\
# make action selection op (outputs int actions, sampled from policy)
# actions = tf.squeeze(tf.multinomial(logits=logits,num_samples=1), axis=1) # ////// Original bloc //////
# actions, log_p_all = BLOC.policy_theta_discrete_space(logits, playground) # \\\\\\ My bloc \\\\\\
# make loss function whose gradient, for the right data, is policy gradient
# weights_ph = tf.placeholder(shape=(None,), dtype=tf.float32) # ////// Original bloc //////
# act_ph = tf.placeholder(shape=(None,), dtype=tf.int32) # ////// Original bloc //////
# action_masks = tf.one_hot(act_ph, n_acts) # ////// Original bloc //////
# log_probs = tf.reduce_sum(action_masks * tf.nn.log_softmax(logits), axis=1) # ////// Original bloc //////
# loss = -tf.reduce_mean(weights_ph * log_probs) # ////// Original bloc //////
# (!) First silent error cause by uneven batch size # \\\\\\ My bloc \\\\\\
# loss = BLOC.discrete_pseudo_loss(log_p_all, act_ph, weights_ph, playground) # \\\\\\ My bloc \\\\\\
reinforce_policy = REINFORCEbrain.REINFORCE_policy(
obs_ph,
act_ph, # \\\\\\ My bloc \\\\\\
weights_ph,
exp_spec,
playground) # \\\\\\ My bloc \\\\\\
(actions, _, loss) = reinforce_policy # \\\\\\ My bloc \\\\\\
# make train op
# train_op = tf.train.AdamOptimizer(learning_rate=lr).minimize(loss) # ////// Original bloc //////
train_op = BLOC.policy_optimizer(
loss,
learning_rate=exp_spec.learning_rate) # \\\\\\ My bloc \\\\\\
# \\\\\\ My bloc \\\\\\
date_now = datetime.now()
run_str = "Run--{}h{}--{}-{}-{}".format(date_now.hour, date_now.minute,
date_now.day, date_now.month,
date_now.year)
# writer = tf_cv1.summary.FileWriter("./graph/{}".format(run_str), tf_cv1.get_default_graph())
writer = tf_cv1.summary.FileWriter(
"test_Z_integration/test_integrationREINFORCE/graph/{}".format(
run_str), tf_cv1.get_default_graph())
the_TRAJECTORY_COLLECTOR = TrajectoryCollector(
exp_spec, playground) # \\\\\\ My bloc \\\\\\
the_UNI_BATCH_COLLECTOR = UniformBatchCollector(
exp_spec.batch_size_in_ts) # \\\\\\ My bloc \\\\\\
# ////// Original bloc //////
# sess = tf.InteractiveSession()
# sess.run(tf.global_variables_initializer())
# \\\\\\ My bloc \\\\\\
tf_cv1.set_random_seed(exp_spec.random_seed)
np.random.seed(exp_spec.random_seed)
with tf_cv1.Session() as sess:
sess.run(tf_cv1.global_variables_initializer()
) # initialize random variable in the computation graph
consol_print_learning_stats.start_the_crazy_experiment()
# for training policy
def train_one_epoch():
consol_print_learning_stats.next_glorious_epoch(
) # \\\\\\ My bloc \\\\\\
# ////// Original bloc //////
# # make some empty lists for logging.
# batch_obs = [] # for observations
# batch_acts = [] # for actions
# batch_weights = [] # for reward-to-go weighting in policy gradient
# batch_rets = [] # for measuring episode returns
# batch_lens = [] # for measuring episode lengths
# ep_rews = [] # list for rewards accrued throughout ep
# reset episode-specific variables
obs = env.reset() # first obs comes from starting distribution
done = False # signal from environment that episode is over
# render first episode of each epoch
finished_rendering_this_epoch = False
consol_print_learning_stats.next_glorious_trajectory(
) # \\\\\\ My bloc \\\\\\
# collect experience by acting in the environment with current policy
while True:
# rendering
if (not finished_rendering_this_epoch) and render:
env.render()
# save obs
# batch_obs.append(obs.copy()) # <-- (!) (Critical) append S_t not S_{t+1} ////// Original bloc //////
# # act in the environment
# act = sess.run(actions, {obs_ph: obs.reshape(1,-1)})[0] # ////// Original bloc //////
# obs, rew, done, _ = env.step(act) # ////// Original bloc //////
step_observation = BLOC.format_single_step_observation(
obs) # \\\\\\ My bloc \\\\\\
action_array = sess.run(actions,
feed_dict={
obs_ph: step_observation
}) # \\\\\\ My bloc \\\\\\
act = blocAndTools.tensorflowbloc.to_scalar(
action_array) # \\\\\\ My bloc \\\\\\
# obs, rew, done, _ = playground.env.step(act) <-- (!) mistake # \\\\\\ My bloc \\\\\\
# (!) Solution to silent error 2: dont ovewrite S_t \\\\\\ My bloc \\\\\\
obs_prime, rew, done, _ = playground.env.step(
act) # <-- (!) Solution \\\\\\ My bloc \\\\\\
# ////// Original bloc //////
# # save action, reward
# batch_acts.append(act)
# ep_rews.append(rew)
# (Critical) | Append the observation S_t that trigered the action A_t is critical. \\\\\\ My bloc \\\\\\
# | If the observation is the one at time S_{t+1}, the agent wont learn \\\\\\ My bloc \\\\\\
the_TRAJECTORY_COLLECTOR.collect_OAR(
obs, act, rew
) # <-- (!) Silent error 2 \\\\\\ My bloc \\\\\\
obs = obs_prime # <-- (!) Solution to silent error 2 \\\\\\ My bloc \\\\\\
if done:
# ////// Original bloc //////
# # if episode is over, record info about episode
# ep_ret, ep_len = sum(ep_rews), len(ep_rews)
# batch_rets.append(ep_ret)
# batch_lens.append(ep_len)
trj_return = the_TRAJECTORY_COLLECTOR.trajectory_ended(
) # \\\\\\ My bloc \\\\\\
the_TRAJECTORY_COLLECTOR.compute_Qvalues_as_rewardToGo()
trj_container = the_TRAJECTORY_COLLECTOR.pop_trajectory_and_reset(
) # \\\\\\ My bloc \\\\\\
the_UNI_BATCH_COLLECTOR.collect(
trj_container) # \\\\\\ My bloc \\\\\\
consol_print_learning_stats.trajectory_training_stat(
the_trajectory_return=trj_return,
timestep=len(
trj_container)) # \\\\\\ My bloc \\\\\\
# the weight for each logprob(a_t|s_t) is reward-to-go from t
# batch_weights += list(reward_to_go(ep_rews)) # ////// Original bloc //////
# batch_weights += BLOC.reward_to_go(ep_rews) # \\\\\\ My bloc \\\\\\
# reset episode-specific variables
obs, done, ep_rews = env.reset(), False, []
consol_print_learning_stats.next_glorious_trajectory(
) # \\\\\\ My bloc \\\\\\
# won't render again this epoch
finished_rendering_this_epoch = True
# ////// Original bloc //////
# # end experience loop if we have enough of it
# if len(batch_obs) > batch_size:
# break
if not the_UNI_BATCH_COLLECTOR.is_not_full(
): # \\\\\\ My bloc \\\\\\
break # \\\\\\ My bloc \\\\\\
# ////// Original bloc //////
# # take a single policy gradient update step
# batch_loss, _ = sess.run([loss, train_op],
# feed_dict={
# obs_ph: np.array(batch_obs),
# act_ph: np.array(batch_acts),
# weights_ph: np.array(batch_weights)
# })
batch_container = the_UNI_BATCH_COLLECTOR.pop_batch_and_reset(
) # \\\\\\ My bloc \\\\\\
(batch_rets, batch_lens) = batch_container.get_basic_metric(
) # \\\\\\ My bloc \\\\\\
batch_obs = batch_container.batch_observations # \\\\\\ My bloc \\\\\\
batch_acts = batch_container.batch_actions # \\\\\\ My bloc \\\\\\
batch_weights = batch_container.batch_Qvalues # \\\\\\ My bloc \\\\\\
feed_dictionary = blocAndTools.tensorflowbloc.build_feed_dictionary(
[obs_ph, act_ph, weights_ph],
# \\\\\\ My bloc \\\\\\
[
batch_obs,
# \\\\\\ My bloc \\\\\\
batch_acts,
batch_weights
]) # \\\\\\ My bloc
# \\\\\\
batch_loss, _ = sess.run(
[loss, train_op], # \\\\\\ My bloc \\\\\\
feed_dict=feed_dictionary) # \\\\\\ My bloc \\\\\\
return batch_loss, batch_rets, batch_lens
# training loop
for i in range(epochs):
batch_loss, batch_rets, batch_lens = train_one_epoch()
mean_return = np.mean(batch_rets)
average_len = np.mean(batch_lens)
# ////// Original bloc //////
# print('epoch: %3d \t loss: %.3f \t return: %.3f \t ep_len: %.3f' %
# (i, batch_loss, mean_return, average_len))
# \\\\\\ My bloc \\\\\\
consol_print_learning_stats.epoch_training_stat(
epoch_loss=batch_loss,
epoch_average_trjs_return=mean_return,
epoch_average_trjs_lenght=average_len,
number_of_trj_collected=0,
total_timestep_collected=0)
yield (i, batch_loss, mean_return, average_len)
print("\n>>> Close session\n")
writer.close()
playground.env.close()
tf_cv1.reset_default_graph()