def train(state_cb,
pub_cmd,
pub_act,
rate,
cost_fn,
logdir=None,
render=False,
learning_rate=1e-3,
onpol_iters=10,
dynamics_iters=60,
batch_size=512,
num_paths_random=10,
num_paths_onpol=10,
num_simulated_paths=10000,
env_horizon=1000,
mpc_horizon=15,
n_layers=2,
size=500,
activation=tf.nn.relu,
output_activation=None):
"""
Arguments:
onpol_iters Number of iterations of onpolicy aggregation for the loop to run.
dynamics_iters Number of iterations of training for the dynamics model
|_ which happen per iteration of the aggregation loop.
batch_size Batch size for dynamics training.
num_paths_random Number of paths/trajectories/rollouts generated
| by a random agent. We use these to train our
|_ initial dynamics model.
num_paths_onpol Number of paths to collect at each iteration of
|_ aggregation, using the Model Predictive Control policy.
num_simulated_paths How many fictitious rollouts the MPC policy
| should generate each time it is asked for an
|_ action.
env_horizon Number of timesteps in each path.
mpc_horizon The MPC policy generates actions by imagining
| fictitious rollouts, and picking the first action
| of the best fictitious rollout. This argument is
| how many timesteps should be in each fictitious
|_ rollout.
n_layers/size/activations Neural network architecture arguments.
"""
logz.configure_output_dir(logdir)
#========================================================
#
# First, we need a lot of data generated by a random
# agent, with which we'll begin to train our dynamics
# model.
rand_controller = RandomController()
paths = sample(state_cb, pub_cmd, pub_act, rate, rand_controller,
num_paths_random, env_horizon, render)
data = paths_to_array(paths)
#========================================================
#
# The random data will be used to get statistics (mean
# and std) for the observations, actions, and deltas
# (where deltas are o_{t+1} - o_t). These will be used
# for normalizing inputs and denormalizing outputs
# from the dynamics network.
#
normalization = compute_normalization(data)
#========================================================
#
# Build dynamics model and MPC controllers.
#
sess = tf.Session()
dyn_model = NNDynamicsModel(n_layers=n_layers,
size=size,
activation=activation,
output_activation=output_activation,
normalization=normalization,
batch_size=batch_size,
iterations=dynamics_iters,
learning_rate=learning_rate,
sess=sess)
mpc_controller = MPCcontroller(dyn_model=dyn_model,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
#========================================================
#
# Tensorflow session building.
#
sess.__enter__()
tf.global_variables_initializer().run()
#========================================================
#
# Take multiple iterations of onpolicy aggregation at each iteration refitting the dynamics model to current dataset and then taking onpolicy samples and aggregating to the dataset.
# Note: You don't need to use a mixing ratio in this assignment for new and old data as described in https://arxiv.org/abs/1708.02596
#
for itr in range(onpol_iters):
# Fit dynamics model
print('Training dynamics model...')
dyn_model.fit(data)
plot_comparison(dyn_model, state_cb, pub_act, pub_cmd, rate)
mpc_controller.dyn_model = dyn_model
costs = []
returns = []
# Do MPC
for i in range(num_paths_onpol):
print('On policy path: %i' % i)
obs_t, obs_tp1, acs_t, rews_t = [], [], [], []
s_t = state_cb.reset(pub_act, pub_cmd)
total_return = 0
for j in range(env_horizon):
# print('Timestep: %i, Return: %g' % (j,total_return))
a_t = mpc_controller.get_action(s_t)
s_tp1, _ = state_cb.step(a_t, pub_act, pub_cmd)
r_t = 0
for i in range(9):
r_t += s_tp1[i * 12] - s_t[i * 12]
total_return += r_t
if render:
env.render()
time.sleep(0.05)
obs_t.append(s_t)
obs_tp1.append(s_tp1)
acs_t.append(a_t)
rews_t.append(r_t)
s_t = s_tp1
path = {
"observations": np.array(obs_t),
"next_observations": np.array(obs_tp1),
"actions": np.array(acs_t),
"rewards": np.array(rews_t)
}
total_cost = path_cost(cost_fn, path)
paths.append(path)
returns.append(total_return)
costs.append(total_cost)
print('Total cost: %g, Total reward: %g' %
(total_cost, total_return))
data = paths_to_array(paths)
normalization = compute_normalization(data)
# Set new normalization statistics for dynamics model
dyn_model.normalization = normalization
# LOGGING
# Statistics for performance of MPC policy using
# our learned dynamics model
logz.log_tabular('Iteration', itr)
# In terms of cost function which your MPC controller uses to plan
logz.log_tabular('AverageCost', np.mean(costs))
logz.log_tabular('StdCost', np.std(costs))
logz.log_tabular('MinimumCost', np.min(costs))
logz.log_tabular('MaximumCost', np.max(costs))
# In terms of true environment reward of your rolled out trajectory using the MPC controller
logz.log_tabular('AverageReturn', np.mean(returns))
logz.log_tabular('StdReturn', np.std(returns))
logz.log_tabular('MinimumReturn', np.min(returns))
logz.log_tabular('MaximumReturn', np.max(returns))
logz.dump_tabular()
def train_PG(
exp_name='',
env_name='',
n_iter=100,
gamma=1.0,
min_timesteps_per_batch=1000,
max_path_length=None,
learning_rate=5e-3,
reward_to_go=False,
animate=True,
logdir=None,
normalize_advantages=False,
nn_baseline=False,
seed=0,
# network arguments
n_layers=1,
size=32,
# mb mpc arguments
model_learning_rate=1e-3,
onpol_iters=10,
dynamics_iters=260,
batch_size=512,
num_paths_random=10,
num_paths_onpol=10,
num_simulated_paths=1000,
env_horizon=1000,
mpc_horizon=10,
m_n_layers=2,
m_size=500,
):
start = time.time()
# Configure output directory for logging
logz.configure_output_dir(logdir)
# Log experimental parameters
args = inspect.getargspec(train_PG)[0]
locals_ = locals()
params = {k: locals_[k] if k in locals_ else None for k in args}
logz.save_params(params)
# Set random seeds
tf.set_random_seed(seed)
np.random.seed(seed)
# Make the gym environment
# env = gym.make(env_name)
env = HalfCheetahEnvNew()
cost_fn = cheetah_cost_fn
activation=tf.nn.relu
output_activation=None
# Is this env continuous, or discrete?
discrete = isinstance(env.action_space, gym.spaces.Discrete)
# Maximum length for episodes
# max_path_length = max_path_length or env.spec.max_episode_steps
max_path_length = max_path_length
# Observation and action sizes
ob_dim = env.observation_space.shape[0]
ac_dim = env.action_space.n if discrete else env.action_space.shape[0]
# Print environment infomation
print("-------- env info --------")
print("Environment name: ", env_name)
print("Action space is discrete: ", discrete)
print("Action space dim: ", ac_dim)
print("Observation space dim: ", ob_dim)
print("Max_path_length ", max_path_length)
#========================================================================================#
# Random data collection
#========================================================================================#
random_controller = RandomController(env)
data_buffer_model = DataBuffer()
data_buffer_ppo = DataBuffer_general(10000, 4)
# sample path
print("collecting random data ..... ")
paths = sample(env,
random_controller,
num_paths=num_paths_random,
horizon=env_horizon,
render=False,
verbose=False)
# add into buffer
for path in paths:
for n in range(len(path['observations'])):
data_buffer_model.add(path['observations'][n], path['actions'][n], path['next_observations'][n])
print("data buffer size: ", data_buffer_model.size)
normalization = compute_normalization(data_buffer_model)
#========================================================================================#
# Tensorflow Engineering: Config, Session, Variable initialization
#========================================================================================#
tf_config = tf.ConfigProto()
tf_config.allow_soft_placement = True
tf_config.intra_op_parallelism_threads =4
tf_config.inter_op_parallelism_threads = 1
sess = tf.Session(config=tf_config)
dyn_model = NNDynamicsModel(env=env,
n_layers=n_layers,
size=size,
activation=activation,
output_activation=output_activation,
normalization=normalization,
batch_size=batch_size,
iterations=dynamics_iters,
learning_rate=learning_rate,
sess=sess)
mpc_controller = MPCcontroller(env=env,
dyn_model=dyn_model,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
policy_nn = policy_network_ppo(sess, ob_dim, ac_dim, discrete, n_layers, size, learning_rate)
if nn_baseline:
value_nn = value_network(sess, ob_dim, n_layers, size, learning_rate)
sess.__enter__() # equivalent to `with sess:`
tf.global_variables_initializer().run()
#========================================================================================#
# Training Loop
#========================================================================================#
total_timesteps = 0
for itr in range(n_iter):
print("********** Iteration %i ************"%itr)
if MPC:
dyn_model.fit(data_buffer_model)
returns = []
costs = []
# Collect paths until we have enough timesteps
timesteps_this_batch = 0
paths = []
while True:
# print("data buffer size: ", data_buffer_model.size)
current_path = {'observations': [], 'actions': [], 'reward': [], 'next_observations':[]}
ob = env.reset()
obs, acs, mpc_acs, rewards = [], [], [], []
animate_this_episode=(len(paths)==0 and (itr % 10 == 0) and animate)
steps = 0
return_ = 0
while True:
# print("steps ", steps)
if animate_this_episode:
env.render()
time.sleep(0.05)
obs.append(ob)
if MPC:
mpc_ac = mpc_controller.get_action(ob)
else:
mpc_ac = random_controller.get_action(ob)
ac = policy_nn.predict(ob, mpc_ac)
ac = ac[0]
if not PG:
ac = mpc_ac
acs.append(ac)
mpc_acs.append(mpc_ac)
current_path['observations'].append(ob)
ob, rew, done, _ = env.step(ac)
current_path['reward'].append(rew)
current_path['actions'].append(ac)
current_path['next_observations'].append(ob)
return_ += rew
rewards.append(rew)
steps += 1
if done or steps > max_path_length:
break
if MPC:
# cost & return
cost = path_cost(cost_fn, current_path)
costs.append(cost)
returns.append(return_)
print("total return: ", return_)
print("costs: ", cost)
# add into buffers
for n in range(len(current_path['observations'])):
data_buffer_model.add(current_path['observations'][n], current_path['actions'][n], current_path['next_observations'][n])
for n in range(len(current_path['observations'])):
data_buffer_ppo.add(current_path['observations'][n], current_path['actions'][n], current_path['reward'][n], current_path['next_observations'][n])
path = {"observation" : np.array(obs),
"reward" : np.array(rewards),
"action" : np.array(acs),
"mpc_action" : np.array(mpc_acs)}
paths.append(path)
timesteps_this_batch += pathlength(path)
# print("timesteps_this_batch", timesteps_this_batch)
if timesteps_this_batch > min_timesteps_per_batch:
break
total_timesteps += timesteps_this_batch
print("data_buffer_ppo.size:", data_buffer_ppo.size)
# Build arrays for observation, action for the policy gradient update by concatenating
# across paths
ob_no = np.concatenate([path["observation"] for path in paths])
ac_na = np.concatenate([path["action"] for path in paths])
mpc_ac_na = np.concatenate([path["mpc_action"] for path in paths])
# Computing Q-values
if reward_to_go:
q_n = []
for path in paths:
for t in range(len(path["reward"])):
t_ = 0
q = 0
while t_ < len(path["reward"]):
if t_ >= t:
q += gamma**(t_-t) * path["reward"][t_]
t_ += 1
q_n.append(q)
q_n = np.asarray(q_n)
else:
q_n = []
for path in paths:
for t in range(len(path["reward"])):
t_ = 0
q = 0
while t_ < len(path["reward"]):
q += gamma**t_ * path["reward"][t_]
t_ += 1
q_n.append(q)
q_n = np.asarray(q_n)
# Computing Baselines
if nn_baseline:
# b_n = sess.run(baseline_prediction, feed_dict={sy_ob_no :ob_no})
b_n = value_nn.predict(ob_no)
b_n = normalize(b_n)
b_n = denormalize(b_n, np.std(q_n), np.mean(q_n))
adv_n = q_n - b_n
else:
adv_n = q_n.copy()
# Advantage Normalization
if normalize_advantages:
adv_n = normalize(adv_n)
# Optimizing Neural Network Baseline
if nn_baseline:
b_n_target = normalize(q_n)
value_nn.fit(ob_no, b_n_target)
# sess.run(baseline_update_op, feed_dict={sy_ob_no :ob_no, sy_baseline_target_n:b_n_target})
# Performing the Policy Update
# policy_nn.fit(ob_no, ac_na, adv_n)
policy_nn.fit(ob_no, ac_na, adv_n, mpc_ac_na)
# sess.run(update_op, feed_dict={sy_ob_no :ob_no, sy_ac_na:ac_na, sy_adv_n:adv_n})
# Log diagnostics
returns = [path["reward"].sum() for path in paths]
ep_lengths = [pathlength(path) for path in paths]
logz.log_tabular("Time", time.time() - start)
logz.log_tabular("Iteration", itr)
logz.log_tabular("AverageReturn", np.mean(returns))
logz.log_tabular("StdReturn", np.std(returns))
logz.log_tabular("MaxReturn", np.max(returns))
logz.log_tabular("MinReturn", np.min(returns))
logz.log_tabular("EpLenMean", np.mean(ep_lengths))
logz.log_tabular("EpLenStd", np.std(ep_lengths))
logz.log_tabular("TimestepsThisBatch", timesteps_this_batch)
logz.log_tabular("TimestepsSoFar", total_timesteps)
logz.dump_tabular()
logz.pickle_tf_vars()
def train(env,
cost_fn,
logdir=None,
render=False,
learning_rate=1e-3,
onpol_iters=10,
dynamics_iters=60,
batch_size=512,
num_paths_random=10,
num_paths_onpol=10,
num_simulated_paths=10000,
env_horizon=1000,
mpc_horizon=15,
n_layers=2,
size=500,
activation=tf.nn.relu,
output_activation=None
):
"""
Arguments:
onpol_iters Number of iterations of onpolicy aggregation for the loop to run.
dynamics_iters Number of iterations of training for the dynamics model
|_ which happen per iteration of the aggregation loop.
batch_size Batch size for dynamics training.
num_paths_random Number of paths/trajectories/rollouts generated
| by a random agent. We use these to train our
|_ initial dynamics model.
num_paths_onpol Number of paths to collect at each iteration of
|_ aggregation, using the Model Predictive Control policy.
num_simulated_paths How many fictitious rollouts the MPC policy
| should generate each time it is asked for an
|_ action.
env_horizon Number of timesteps in each path.
mpc_horizon The MPC policy generates actions by imagining
| fictitious rollouts, and picking the first action
| of the best fictitious rollout. This argument is
| how many timesteps should be in each fictitious
|_ rollout.
n_layers/size/activations Neural network architecture arguments.
"""
logz.configure_output_dir(logdir)
#========================================================
#
# First, we need a lot of data generated by a random
# agent, with which we'll begin to train our dynamics
# model.
random_controller = RandomController(env)
""" YOUR CODE HERE """
# sample random paths
data = sample(env, random_controller, num_paths_random, env_horizon)
#========================================================
#
# The random data will be used to get statistics (mean
# and std) for the observations, actions, and deltas
# (where deltas are o_{t+1} - o_t). These will be used
# for normalizing inputs and denormalizing outputs
# from the dynamics network.
#
normalization = compute_normalization(data)
#========================================================
#
# Build dynamics model and MPC controllers.
#
sess = tf.Session()
dyn_model = NNDynamicsModel(env=env,
n_layers=n_layers,
size=size,
activation=activation,
output_activation=output_activation,
normalization=normalization,
batch_size=batch_size,
iterations=dynamics_iters,
learning_rate=learning_rate,
sess=sess)
mpc_controller = MPCcontroller(env=env,
dyn_model=dyn_model,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
#========================================================
#
# Tensorflow session building.
#
sess.__enter__()
tf.global_variables_initializer().run()
#========================================================
#
# Take multiple iterations of onpolicy aggregation at each iteration refitting the dynamics model to current dataset and then taking onpolicy samples and aggregating to the dataset.
# Note: You don't need to use a mixing ratio in this assignment for new and old data as described in https://arxiv.org/abs/1708.02596
#
for itr in range(onpol_iters):
""" YOUR CODE HERE """
dyn_model.fit(data)
paths = []
# iteration over paths
for n_path in range(num_paths_onpol):
ob = env.reset()
obs, n_obs, acs, rewards = [], [], [], []
for n_horz in range(env_horizon):
obs.append(ob)
ac = mpc_controller.get_action(ob)
acs.append(ac)
ob, rew, done, _ = env.step(ac)
n_obs.append(ob)
rewards.append(rew)
cost = trajectory_cost_fn(cost_fn, np.array(obs), np.array(acs), np.array(n_obs))
path = {"observations" : np.array(obs),
"next_observations" : np.array(n_obs),
"rewards" : np.array(rewards),
"actions" : np.array(acs),
"costs" : cost}
paths.append(path)
data.append(path)
returns = [path["rewards"].sum() for path in paths]
costs = [path["costs"] for path in paths]
# LOGGING
# Statistics for performance of MPC policy using
# our learned dynamics model
logz.log_tabular('Iteration', itr)
# In terms of cost function which your MPC controller uses to plan
logz.log_tabular('AverageCost', np.mean(costs))
logz.log_tabular('StdCost', np.std(costs))
logz.log_tabular('MinimumCost', np.min(costs))
logz.log_tabular('MaximumCost', np.max(costs))
# In terms of true environment reward of your rolled out trajectory using the MPC controller
logz.log_tabular('AverageReturn', np.mean(returns))
logz.log_tabular('StdReturn', np.std(returns))
logz.log_tabular('MinimumReturn', np.min(returns))
logz.log_tabular('MaximumReturn', np.max(returns))
logz.dump_tabular()
def train(env,
cost_fn,
logdir=None,
render=False,
learning_rate=1e-3,
onpol_iters=10,
dynamics_iters=60,
batch_size=512,
num_paths_random=10,
num_paths_onpol=10,
num_simulated_paths=10000,
env_horizon=1000,
mpc_horizon=15,
n_layers=2,
size=500,
activation=tf.nn.relu,
output_activation=None
):
# tracker = SummaryTracker()
"""
Arguments:
onpol_iters Number of iterations of onpolicy aggregation for the loop to run.
dynamics_iters Number of iterations of training for the dynamics model
|_ which happen per iteration of the aggregation loop.
batch_size Batch size for dynamics training.
num_paths_random Number of paths/trajectories/rollouts generated
| by a random agent. We use these to train our
|_ initial dynamics model.
num_paths_onpol Number of paths to collect at each iteration of
|_ aggregation, using the Model Predictive Control policy.
num_simulated_paths How many fictitious rollouts the MPC policy
| should generate each time it is asked for an
|_ action.
env_horizon Number of timesteps in each path.
mpc_horizon The MPC policy generates actions by imagining
| fictitious rollouts, and picking the first action
| of the best fictitious rollout. This argument is
| how many timesteps should be in each fictitious
|_ rollout.
n_layers/size/activations Neural network architecture arguments.
"""
logz.configure_output_dir(logdir)
#========================================================
#
# First, we need a lot of data generated by a random
# agent, with which we'll begin to train our dynamics
# model.
""" YOUR CODE HERE """
# Print env info
print("-------- env info --------")
print("observation_space: ", env.observation_space.shape)
print("action_space: ", env.action_space.shape)
print(" ")
random_controller = RandomController(env)
data_buffer = DataBuffer()
bc_data_buffer = DataBuffer_SA(BC_BUFFER_SIZE)
# sample path
print("collecting random data ..... ")
paths = sample(env,
random_controller,
num_paths=num_paths_random,
horizon=env_horizon,
render=False,
verbose=False)
# add into buffer
for path in paths:
for n in range(len(path['observations'])):
data_buffer.add(path['observations'][n], path['actions'][n], path['next_observations'][n])
#========================================================
#
# The random data will be used to get statistics (mean
# and std) for the observations, actions, and deltas
# (where deltas are o_{t+1} - o_t). These will be used
# for normalizing inputs and denormalizing outputs
# from the dynamics network.
#
print("data buffer size: ", data_buffer.size)
normalization = compute_normalization(data_buffer)
#========================================================
#
# Build dynamics model and MPC controllers and Behavioral cloning network.
#
sess = tf.Session()
dyn_model = NNDynamicsModel(env=env,
n_layers=n_layers,
size=size,
activation=activation,
output_activation=output_activation,
normalization=normalization,
batch_size=batch_size,
iterations=dynamics_iters,
learning_rate=learning_rate,
sess=sess)
mpc_controller = MPCcontroller(env=env,
dyn_model=dyn_model,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
bc_net = BCnetwork(sess, env, BATCH_SIZE_BC, learning_rate)
mpc_controller_bc = MPCcontroller_BC(env=env,
dyn_model=dyn_model,
bc_network=bc_net,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
#========================================================
#
# Tensorflow session building.
#
sess.__enter__()
tf.global_variables_initializer().run()
# init or load checkpoint with saver
saver = tf.train.Saver()
checkpoint = tf.train.get_checkpoint_state(CHECKPOINT_DIR)
if checkpoint and checkpoint.model_checkpoint_path and LOAD_MODEL:
saver.restore(sess, checkpoint.model_checkpoint_path)
print("checkpoint loaded:", checkpoint.model_checkpoint_path)
else:
print("Could not find old checkpoint")
if not os.path.exists(CHECKPOINT_DIR):
os.mkdir(CHECKPOINT_DIR)
#========================================================
#
# Take multiple iterations of onpolicy aggregation at each iteration refitting the dynamics model to current dataset and then taking onpolicy samples and aggregating to the dataset.
# Note: You don't need to use a mixing ratio in this assignment for new and old data as described in https://arxiv.org/abs/1708.02596
#
for itr in range(onpol_iters):
""" YOUR CODE HERE """
print("onpol_iters: ", itr)
dyn_model.fit(data_buffer)
saver.save(sess, CHECKPOINT_DIR)
returns = []
costs = []
for w in range(num_paths_onpol):
print("paths_onpol: ", w, " running.....")
print("data buffer size: ", data_buffer.size)
st = env.reset_model()
path = {'observations': [], 'actions': [], 'next_observations':[]}
# tracker.print_diff()
return_ = 0
for i in range(env_horizon):
if render:
env.render()
# print("env_horizon: ", i)
if BEHAVIORAL_CLONING:
if bc_data_buffer.size > 2000:
at = mpc_controller_bc.get_action(st)
else:
at = mpc_controller.get_action(st)
else:
at = mpc_controller.get_action(st)
# at = random_controller.get_action(st)
st_next, env_reward, _, _ = env._step(at)
path['observations'].append(st)
path['actions'].append(at)
path['next_observations'].append(st_next)
st = st_next
return_ += env_reward
# cost & return
cost = path_cost(cost_fn, path)
costs.append(cost)
returns.append(return_)
print("total return: ", return_)
print("costs: ", cost)
# add into buffers
for n in range(len(path['observations'])):
data_buffer.add(path['observations'][n], path['actions'][n], path['next_observations'][n])
bc_data_buffer.add(path['observations'][n], path['actions'][n])
if BEHAVIORAL_CLONING:
behavioral_cloning(sess, env, bc_net, mpc_controller, env_horizon, bc_data_buffer, Training_epoch=1000)
# LOGGING
# Statistics for performance of MPC policy using
# our learned dynamics model
logz.log_tabular('Iteration', itr)
# logz.log_tabular('Average_BC_Return', np.mean(bc_returns))
# In terms of cost function which your MPC controller uses to plan
logz.log_tabular('AverageCost', np.mean(costs))
logz.log_tabular('StdCost', np.std(costs))
logz.log_tabular('MinimumCost', np.min(costs))
logz.log_tabular('MaximumCost', np.max(costs))
# In terms of true environment reward of your rolled out trajectory using the MPC controller
logz.log_tabular('AverageReturn', np.mean(returns))
logz.log_tabular('StdReturn', np.std(returns))
logz.log_tabular('MinimumReturn', np.min(returns))
logz.log_tabular('MaximumReturn', np.max(returns))
logz.dump_tabular()
def train(
env,
cost_fn,
logdir=None,
render=False,
learning_rate=1e-3,
onpol_iters=10,
dynamics_iters=60,
batch_size=512,
num_paths_random=10,
num_paths_onpol=10,
num_simulated_paths=1000,
env_horizon=1000,
mpc_horizon=15,
n_layers=2,
size=500,
activation=tf.nn.relu,
output_activation=None,
):
"""
Arguments:
onpol_iters Number of iterations of onpolicy aggregation for the loop to run.
dynamics_iters Number of iterations of training for the dynamics model
|_ which happen per iteration of the aggregation loop.
batch_size Batch size for dynamics training.
num_paths_random Number of paths/trajectories/rollouts generated
| by a random agent. We use these to train our
|_ initial dynamics model.
num_paths_onpol Number of paths to collect at each iteration of
|_ aggregation, using the Model Predictive Control policy.
num_simulated_paths How many fictitious rollouts the MPC policy
| should generate each time it is asked for an
|_ action.
env_horizon Number of timesteps in each path.
mpc_horizon The MPC policy generates actions by imagining
| fictitious rollouts, and picking the first action
| of the best fictitious rollout. This argument is
| how many timesteps should be in each fictitious
|_ rollout.
n_layers/size/activations Neural network architecture arguments.
"""
logz.configure_output_dir(logdir)
#========================================================
#
# First, we need a lot of data generated by a random
# agent, with which we'll begin to train our dynamics
# model.
random_controller = RandomController(env)
""" YOUR CODE HERE """
paths_rand = sample(env,
random_controller,
num_paths=num_paths_random,
horizon=env_horizon,
render=render,
verbose=False)
#========================================================
#
# The random data will be used to get statistics (mean
# and std) for the observations, actions, and deltas
# (where deltas are o_{t+1} - o_t). These will be used
# for normalizing inputs and denormalizing outputs
# from the dynamics network.
#
""" YOUR CODE HERE """
normalization = compute_normalization(paths_rand)
gamma = 0.99
#========================================================
#
# Build dynamics model and MPC controllers.
#
sess = tf.Session()
dyn_model = NNDynamicsModel(env=env,
n_layers=n_layers,
size=size,
activation=activation,
output_activation=output_activation,
normalization=normalization,
batch_size=batch_size,
iterations=dynamics_iters,
learning_rate=learning_rate,
sess=sess)
mpc_controller = MPCcontroller(env=env,
dyn_model=dyn_model,
horizon=mpc_horizon,
cost_fn=cost_fn,
num_simulated_paths=num_simulated_paths)
#========================================================
#
# Tensorflow session building.
#
sess.__enter__()
tf.global_variables_initializer().run()
#========================================================
#
# Take multiple iterations of onpolicy aggregation at each iteration refitting the dynamics model to current dataset and then taking onpolicy samples and aggregating to the dataset.
# Note: You don't need to use a mixing ratio in this assignment for new and old data as described in https://arxiv.org/abs/1708.02596
#
# prefit dynamic before on policy dagger:
print("****** Pretrain dynamic Model *******")
losses = []
obs_rand = np.concatenate([path["observation"] for path in paths_rand])
action_rand = np.concatenate([path["action"] for path in paths_rand])
next_ob_rand = np.concatenate([path["obs_next"] for path in paths_rand])
data_size_rand = obs_rand.shape[0]
for i in range(1000):
# obtain batch size from random policy
batch_idx_rand = np.random.randint(data_size_rand, size=batch_size)
batch_ob_rand = obs_rand[batch_idx_rand, :]
batch_ac_rand = action_rand[batch_idx_rand, :]
batch_nxt_rand = next_ob_rand[batch_idx_rand, :]
# obtain batch size from on policy
batch_ob = np.copy(batch_ob_rand)
batch_ac = np.copy(batch_ac_rand)
batch_nxt = np.copy(batch_nxt_rand)
loss = dyn_model.fit(batch_ob, batch_ac, batch_nxt)
losses.append(loss)
if (i % 20 == 0):
print('loss', loss)
costs = []
returns = []
paths_rl = []
for itr in range(onpol_iters):
""" YOUR CODE HERE """
# fit dynamic model
if itr > 0:
obs_rl = np.concatenate([path["observation"] for path in paths_rl])
action_rl = np.concatenate([path["action"] for path in paths_rl])
next_ob_rl = np.concatenate(
[path["obs_next"] for path in paths_rl])
obs_rand = np.concatenate([path["observation"] for path in paths_rand])
action_rand = np.concatenate([path["action"] for path in paths_rand])
next_ob_rand = np.concatenate(
[path["obs_next"] for path in paths_rand])
# print obs[128,:].shape
data_size_rand = obs_rand.shape[0]
if itr > 0:
data_size_rl = obs_rl.shape[0]
# batch_size=128
losses = []
# fit model function
for i in range(dynamics_iters):
# obtain batch size from random policy
batch_idx_rand = np.random.randint(data_size_rand,
size=batch_size / 20)
batch_ob_rand = obs_rand[batch_idx_rand, :]
batch_ac_rand = action_rand[batch_idx_rand, :]
batch_nxt_rand = next_ob_rand[batch_idx_rand, :]
# obtain batch size from on policy
if itr > 0:
batch_idx_rl = np.random.randint(data_size_rl,
size=batch_size * 19 / 20)
batch_ob_rl = obs_rl[batch_idx_rl, :]
batch_ac_rl = action_rl[batch_idx_rl, :]
batch_nxt_rl = next_ob_rl[batch_idx_rl, :]
# mix them
batch_ob = np.concatenate((batch_ob_rand, batch_ob_rl))
batch_ac = np.concatenate((batch_ac_rand, batch_ac_rl))
batch_nxt = np.concatenate((batch_nxt_rand, batch_nxt_rl))
else:
batch_ob = np.copy(batch_ob_rand)
batch_ac = np.copy(batch_ac_rand)
batch_nxt = np.copy(batch_nxt_rand)
loss = dyn_model.fit(batch_ob, batch_ac, batch_nxt)
losses.append(loss)
# if(i%20==0):
# print('loss', loss)
print("on policy dagger ", itr)
ob = env.reset()
observes, acs, rewards, obs_2, returns = [], [], [], [], []
steps = 0
g = 0
max_path_length = mpc_controller.horizon
timesteps_this_batch = 0
while True:
while True:
observes.append(ob)
ac = mpc_controller.get_action(ob)
# print ac
acs.append(ac)
# print ac
ob, rew, done, _ = env.step(ac)
g += rew * gamma**steps
obs_2.append(ob)
rewards.append(rew)
returns.append(g)
steps += 1
if done or steps > max_path_length:
terminated = done
break
path = {
"observation": np.array(observes),
"reward": np.array(rewards),
"action": np.array(acs),
"obs_next": np.array(obs_2),
"return": np.array(returns)
}
paths_rl.append(path)
timesteps_this_batch += pathlength(path)
print g
if timesteps_this_batch > batch_size:
break
trajectory_cost = trajectory_cost_fn(cheetah_cost_fn,
path["observation"],
path["action"], path["obs_next"])
costs.append(trajectory_cost)
returns.append(path["return"][-1])
# print batch_ob.shape
# LOGGING
# Statistics for performance of MPC policy using
# our learned dynamics model
logz.log_tabular('Iteration', itr)
# In terms of cost function which your MPC controller uses to plan
logz.log_tabular('AverageCost', np.mean(costs))
logz.log_tabular('StdCost', np.std(costs))
logz.log_tabular('MinimumCost', np.min(costs))
logz.log_tabular('MaximumCost', np.max(costs))
# In terms of true environment reward of your rolled out trajectory using the MPC controller
logz.log_tabular('AverageReturn', np.mean(returns))
logz.log_tabular('StdReturn', np.std(returns))
logz.log_tabular('MinimumReturn', np.min(returns))
logz.log_tabular('MaximumReturn', np.max(returns))
logz.dump_tabular()
def main():
nb_total_steps = 1000
nb_iterations = 40
hidden_layers = [256, 256]
writer = tensorboardX.SummaryWriter()
args = parse_args(__doc__, ['env'])
env = gym.make(args.env)
ctrl = rand_ctrl = RandomController(env)
# ipdb.set_trace()
print('#inputs : %d' % ctrl.nb_inputs())
print('#actions: %d' % ctrl.nb_actions())
# f_net = make_net(
# [ctrl.nb_inputs() + ctrl.nb_actions()] + hidden_layers + [ctrl.nb_inputs()],
# [nn.ReLU() for _ in hidden_layers],
# )
f_net = MOENetwork(
nb_inputs=ctrl.nb_inputs() + ctrl.nb_actions(),
nb_experts=4,
gait_layers=[64],
expert_layers=[64, ctrl.nb_inputs()],
)
data = collect_data(env, ctrl, nb_total_steps*10)
# ipdb.set_trace()
dynamics = DynamicsModel(env, f_net, data.get_all(), writer=writer)
# cost_func = lambda s,a,sn: -sn[3].item() # refers to vx
cost_func = get_cost(args.env) # refers to vx
# data.calc_normalizations()
# dynamics.fit(data)
mpc_ctrl = MPCcontroller(env, dynamics.predict, cost_func, num_simulated_paths=100, horizon=10, num_mpc_steps=10)
eval_args = EvaluationArgs(nb_burnin_steps=4, nb_episodes=10, horizons=[1, 2, 4, 8, 16, 32])
for i in range(nb_iterations):
print('Iteration', i)
new_data = collect_data(env, ctrl, nb_total_steps)
dynamics.fit(*new_data.get_all())
data.extend(new_data)
dynamics.fit(*data.sample(sample_size=4*nb_total_steps))
evaluate_and_log_dynamics(
dynamics.predict, env, rand_ctrl, writer=writer, i_step=i, args=eval_args
)
evaluate_and_log_dynamics(
dynamics.predict, env, mpc_ctrl, writer=writer, i_step=i, args=eval_args
)
# dynamics.fit(*data.get_all())
if random.random() > 0.5:
ctrl = rand_ctrl
else:
ctrl = mpc_ctrl
env = gym.make(args.env)
ctrl = MPCcontroller(env, dynamics.predict, cost_func, num_simulated_paths=1000, num_mpc_steps=4)
# TODO
env.render(mode='human')
obs = env.reset()
for _ in range(100):
# time.sleep(1. / 60.)
obs, r, done, _ = env.step(ctrl.get_action(obs))
# print(' ', cost_func(obs))
if done:
print("done:", r, obs)
time.sleep(1)
ctrl.reset()
obs = env.reset()
ipdb.set_trace()
