algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.batch_size,
max_path_length=env.horizon,
n_itr=args.num_epochs,
discount=0.99,
step_size=args.step_size,
optimizer=ConjugateGradientOptimizer(
reg_coeff=args.reg_coeff,
hvp_approach=FiniteDifferenceHvp(base_eps=args.reg_coeff)))
custom_train(algo, sess=sess)
rollouts = algo.obtain_samples(args.num_epochs + 1)
logger.log("Average reward for training rollouts on (%s): %f +- %f " %
(env_name, np.mean([np.sum(p['rewards'])
for p in rollouts]),
np.std([np.sum(p['rewards']) for p in rollouts])))
# Final evaluation on all environments using the learned policy
total_rollouts = []
for env_name, env in envs:
rollouts = []
for i in range(args.num_final_rollouts):
rollout = rollout_policy(policy,
env,
max_path_length=env.horizon,
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=50000, # Mujoco tasks need 20000-50000
max_path_length=env.horizon, # And 500
n_itr=iters,
discount=0.99,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(hvp_approach=FiniteDifferenceHvp(base_eps=1e-5))
)
# run_experiment_lite(
# ,
# n_parallel=1,
# snapshot_mode="last",
# seed=1
# )
with tf.Session() as sess:
algo.train(sess=sess)
rollouts = algo.obtain_samples(iters+1)
print("Average reward for expert rollouts: %f" % np.mean([np.sum(p['rewards']) for p in rollouts]))
# import pdb; pdb.set_trace()
with open(args.expert_rollout_pickle_path, "wb") as output_file:
pickle.dump(rollouts, output_file)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=args.batch_size,
max_path_length=env.horizon,
n_itr=args.num_epochs,
discount=0.99,
step_size=args.step_size,
optimizer=ConjugateGradientOptimizer(reg_coeff=args.reg_coeff, hvp_approach=FiniteDifferenceHvp(base_eps=args.reg_coeff))
)
custom_train(algo, sess=sess)
rollouts = algo.obtain_samples(args.num_epochs + 1)
logger.log("Average reward for training rollouts on (%s): %f +- %f " % (env_name, np.mean([np.sum(p['rewards']) for p in rollouts]), np.std([np.sum(p['rewards']) for p in rollouts])))
# Final evaluation on all environments using the learned policy
total_rollouts = []
for env_name, env in envs:
rollouts = []
for i in range(args.num_final_rollouts):
rollout = rollout_policy(policy, env, max_path_length=env.horizon, speedup=1, get_image_observations=True, animated=True)
rollouts.append(rollout)
total_rollouts.append(rollout)
logger.log("Average reward for eval rollouts on (%s): %f +- %f " % (env_name, np.mean([np.sum(p['rewards']) for p in rollouts]), np.std([np.sum(p['rewards']) for p in rollouts])))
def run_experiment(expert_rollout_pickle_path,
trained_policy_pickle_path,
env,
cost_trainer_type,
iterations=30,
num_frames=1,
traj_len=200,
config={}):
# Load the expert rollouts into memory
expert_rollouts = load_expert_rollouts(expert_rollout_pickle_path)
# In the case that we only have one expert rollout in the file
if type(expert_rollouts) is dict:
expert_rollouts = [expert_rollouts]
#TODO: make this configurable
expert_rollouts = [
shorten_tensor_dict(x, traj_len) for x in expert_rollouts
]
# import pdb; pdb.set_trace()
# Sanity check, TODO: should prune any "expert" rollouts with suboptimal reward?
print("Average reward for expert rollouts: %f" %
np.mean([np.sum(p['rewards']) for p in expert_rollouts]))
if "transformers" in config and len(config["transformers"]) > 0:
print("Transforming expert rollouts...")
for rollout in tqdm(expert_rollouts):
transformed_observations = []
for ob in tqdm(rollout["observations"]):
for transformer in config["transformers"]:
ob = transformer.transform(ob)
transformed_observations.append(ob)
rollout["observations"] = np.array(transformed_observations)
# Handle both flattened state input and image input
# TODO: this could be done better by looking at just the shape and determining from that
if config["img_input"]:
obs_dims = expert_rollouts[0]['observations'][0].shape
else:
# import pdb; pdb.set_trace()
obs_dims = len(expert_rollouts[0]['observations'][0])
if "num_novice_rollouts" in config:
number_of_sample_trajectories = config["num_novice_rollouts"]
else:
number_of_sample_trajectories = len(expert_rollouts)
print(number_of_sample_trajectories)
# Choose a policy (Conv based on images, mlp based on states)
# TODO: may also have to switch out categorical for something else in continuous state spaces??
# Let's just avoid that for now?
if config[
"img_input"]: # TODO: unclear right now if this even works ok. get poor results early on.
policy = CategoricalConvPolicy(
name="policy",
env_spec=env.spec,
conv_filters=[32, 64, 64],
conv_filter_sizes=[3, 3, 3],
conv_strides=[1, 1, 1],
conv_pads=['SAME', 'SAME', 'SAME'],
# The neural network policy should have two hidden layers, each with 100 hidden units each (see RLGAN paper)
hidden_sizes=[200, 200])
elif type(env.spec.action_space) == Discrete:
policy = CategoricalMLPPolicy(
name="policy",
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 100 hidden units each (see RLGAN paper)
hidden_sizes=(400, 300))
else:
policy = GaussianMLPPolicy(name="policy",
env_spec=env.spec,
hidden_sizes=(100, 50, 25))
if config["img_input"]:
# TODO: right now the linear feature baseline is too computationally expensive to actually use
# with full image inputs, so for now just use the zero baseline
baseline = ZeroBaseline(env_spec=env.spec)
else:
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env=env,
policy=policy,
baseline=baseline,
batch_size=number_of_sample_trajectories *
traj_len, # This is actually used internally by the sampler. We make use of this sampler to generate our samples, hence we pass it here
max_path_length=
traj_len, # same with this value. A cleaner way may be to create our own sampler, but for now doing it this way..
n_itr=40,
discount=0.995,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5),
max_backtracks=40))
# Prune the number of rollouts if that option is enabled
if "num_expert_rollouts" in config:
rollouts_to_use = min(config["num_expert_rollouts"],
len(expert_rollouts))
expert_rollouts = expert_rollouts[:rollouts_to_use]
print("Only using %d expert rollouts" % rollouts_to_use)
true_rewards = []
actual_rewards = []
# Extract observations to a tensor
expert_rollouts_tensor = tensor_utils.stack_tensor_list(
[path["observations"] for path in expert_rollouts])
if "oversample" in config and config["oversample"]:
oversample_rate = max(
int(number_of_sample_trajectories / len(expert_rollouts_tensor)),
1.)
expert_rollouts_tensor = expert_rollouts_tensor.repeat(oversample_rate,
axis=0)
print("oversampling %d times to %d" %
(oversample_rate, len(expert_rollouts_tensor)))
with tf.Session() as sess:
algo.start_worker()
cost_trainer = cost_trainer_type([num_frames, obs_dims], config=config)
trainer = Trainer(env=env,
sess=sess,
cost_approximator=cost_trainer,
cost_trainer=cost_trainer,
novice_policy=policy,
novice_policy_optimizer=algo,
num_frames=num_frames)
sess.run(tf.global_variables_initializer())
for iter_step in range(0, iterations):
dump_data = (iter_step == (
iterations -
1)) and config["generate_option_graphs"] # is last iteration
true_reward, actual_reward = trainer.step(
dump_datapoints=dump_data,
config=config,
expert_horizon=traj_len,
number_of_sample_trajectories=number_of_sample_trajectories)
true_rewards.append(true_reward)
actual_rewards.append(actual_reward)
# run a rollout for the video
if "recording_env" in config:
novice_rollouts = rollout_policy(policy,
config["recording_env"],
get_image_observations=False,
max_path_length=200)
novice_rollouts = algo.obtain_samples(iter_step)
rollout_rewards = [np.sum(x['rewards']) for x in novice_rollouts]
print("Reward stats for final policy: %f +/- %f " %
(np.mean(rollout_rewards), np.std(rollout_rewards)))
# save the novice policy learned
with open(trained_policy_pickle_path, "wb") as output_file:
pickle.dump(policy, output_file)
# TODO: also save the reward function?
algo.shutdown_worker()
second_true_rewards = []
second_actual_rewards = []
# Do our transfer learning task here:
# TODO: move this to a separate script and save the learned weights
if config['second_env'] is not None:
with tf.variable_scope("second_policy"):
#TODO: remove gross copypasta
if not config["reset_second_policy"]:
second_policy = Serializable.clone(
policy) # TODO: start with a fresh policy
else:
if config[
"img_input"]: # TODO: unclear right now if this even works ok. get poor results early on.
second_policy = CategoricalConvPolicy(
name="policy",
env_spec=config["second_env"].spec,
conv_filters=[32, 64, 64],
conv_filter_sizes=[3, 3, 3],
conv_strides=[1, 1, 1],
conv_pads=['SAME', 'SAME', 'SAME'],
# The neural network policy should have two hidden layers, each with 100 hidden units each (see RLGAN paper)
hidden_sizes=[200, 200])
elif type(env.spec.action_space) == Discrete:
second_policy = CategoricalMLPPolicy(
name="policy",
env_spec=config["second_env"].spec,
# The neural network policy should have two hidden layers, each with 100 hidden units each (see RLGAN paper)
hidden_sizes=(400, 300))
else:
second_policy = GaussianMLPPolicy(
name="policy",
env_spec=config["second_env"].spec,
hidden_sizes=(100, 50, 25))
if config["img_input"]:
# TODO: right now the linear feature baseline is too computationally expensive to actually use
# with full image inputs, so for now just use the zero baseline
baseline = ZeroBaseline(env_spec=config["second_env"].spec)
else:
baseline = LinearFeatureBaseline(
env_spec=config["second_env"].spec)
algo = TRPO(
env=config["second_env"],
policy=second_policy,
baseline=baseline,
batch_size=number_of_sample_trajectories *
traj_len, # This is actually used internally by the sampler. We make use of this sampler to generate our samples, hence we pass it here
max_path_length=
traj_len, # same with this value. A cleaner way may be to create our own sampler, but for now doing it this way..
n_itr=40,
discount=0.995,
step_size=0.01,
optimizer=ConjugateGradientOptimizer(
hvp_approach=FiniteDifferenceHvp(base_eps=1e-5),
max_backtracks=40))
if not config["stop_disc_training_on_second_run"] and config[
"use_prev_options_relearn_mixing_func"]:
# If we're not retraining the discriminator at all in the transfer learning step,
# just keep the old network
options = cost_trainer.disc.discriminator_options
cost_trainer.disc._remake_network_from_disc_options(
options,
stop_gradients=(not config["retrain_options"]),
num_extra_options=config["num_extra_options_on_transfer"])
trainer = Trainer(
env=config['second_env'],
sess=sess,
cost_approximator=cost_trainer,
cost_trainer=cost_trainer,
novice_policy=second_policy,
novice_policy_optimizer=algo,
num_frames=num_frames,
train_disc=(not config["stop_disc_training_on_second_run"]))
algo.start_worker()
initialize_uninitialized(sess)
for iter_step in range(0, iterations):
# import pdb; pdb.set_trace()
dump_data = (iter_step == (iterations - 1)) and config[
"generate_option_graphs"] # is last iteration
true_reward, actual_reward = trainer.step(
expert_rollouts_tensor=expert_rollouts_tensor,
dump_datapoints=dump_data,
config=config,
expert_horizon=traj_len,
number_of_sample_trajectories=number_of_sample_trajectories
)
second_true_rewards.append(true_reward)
second_actual_rewards.append(actual_reward)
# run a rollout for the video
if "recording_env" in config:
novice_rollouts = rollout_policy(
second_policy,
config["recording_env"],
get_image_observations=False,
max_path_length=traj_len)
novice_rollouts = algo.obtain_samples(iter_step)
rollout_rewards = [np.sum(x['rewards']) for x in novice_rollouts]
print("Reward stats for final policy: %f +/- %f " %
(np.mean(rollout_rewards), np.std(rollout_rewards)))
# save the novice policy learned
with open(trained_policy_pickle_path, "wb") as output_file:
pickle.dump(second_policy, output_file)
algo.shutdown_worker()
return true_rewards, actual_rewards, second_true_rewards, second_actual_rewards
