def learn(self):
logger.info("Training")
n_steps = 0
# best_success_rate = 0.
for epoch in range(self.args.n_epochs):
residual_losses = []
for _ in range(self.args.n_cycles):
# Collect trajectories
self.controller.reconfigure_heuristic(self.get_residual)
n_steps += self.collect_trajectories(
self.args.num_rollouts_per_mpi)
# Update residual
logger.debug("Updating")
for _ in range(self.args.n_batches):
residual_loss = self._update_residual()
residual_losses.append(
residual_loss.detach().cpu().numpy())
logger.debug('Loss', residual_loss)
self._update_target_network(self.residual_target,
self.residual)
success_rate = self.eval_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print(
'[{}] epoch is: {}, Num steps: {}, eval success rate is: {:.3f}'
.format(datetime.now(), epoch, n_steps, success_rate))
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_steps', n_steps)
logger.record_tabular('success_rate', success_rate)
logger.record_tabular('residual_loss',
np.mean(residual_losses))
logger.dump_tabular()
def eval_agent(self):
total_success_rate = []
for _ in range(self.args.n_test_rollouts):
per_success_rate = []
self.env.reset()
for _ in range(self.env_params['max_timesteps']):
# convert the actions
actions = np.zeros(self.env_params['action'])
observation_new, _, _, info = self.env.step(actions)
per_success_rate.append(info['is_success'])
total_success_rate.append(per_success_rate)
total_success_rate = np.array(total_success_rate)
local_success_rate = np.mean(total_success_rate[:, -1])
global_success_rate = MPI.COMM_WORLD.allreduce(
local_success_rate, op=MPI.SUM)
success_rate = global_success_rate / MPI.COMM_WORLD.Get_size()
if MPI.COMM_WORLD.Get_rank() == 0:
logger.record_tabular('success_rate', success_rate)
logger.dump_tabular()
return success_rate
def learn_online_in_real_world(self, max_timesteps=None):
# If any pre-existing model is given, load it
if self.args.load_dir:
self.load()
# Reset the environment
observation = self.env.reset()
# Count of total number of steps
total_n_steps = 0
while True:
# Rollout for a few steps to collect some transitions
n_steps, final_observation, done = self.online_rollout(observation)
# Update the counter
total_n_steps += n_steps
# Check if we have reached the goal
if done:
break
# Batch updates
losses = []
# for _ in range(self.args.planning_rollout_length):
for _ in range(self.args.n_online_planning_updates):
# Update the state-action value residual
loss = self._update_state_action_value_residual()
losses.append(loss)
# Update the target network
self._update_target_network(self.state_action_value_target_residual,
self.state_action_value_residual)
# Log
logger.record_tabular('n_steps', total_n_steps)
logger.record_tabular('residual_loss', np.mean(losses))
# logger.dump_tabular()
# Move to next iteration
observation = copy.deepcopy(final_observation)
if max_timesteps and total_n_steps >= max_timesteps:
break
return total_n_steps
def learn_online_in_real_world(self, max_timesteps=None):
# If any pre-existing model is given, load it
if self.args.load_dir:
self.load()
# Reset the environment
observation = self.env.reset()
# Configure heuristic for controller
self.controller.reconfigure_heuristic(
lambda obs: get_state_value_residual(obs, self.preproc_inputs, self
.state_value_residual))
# Configure dynamics for controller
if self.args.agent == 'rts':
self.controller.reconfigure_discrepancy(
lambda obs, ac: get_discrepancy_neighbors(
obs, ac, self.construct_4d_point, self.kdtrees, self.args.
neighbor_radius))
# Configure dynamics for controller
if self.args.agent == 'mbpo' or self.args.agent == 'mbpo_knn' or self.args.agent == 'mbpo_gp':
self.controller.reconfigure_residual_dynamics(
self.get_residual_dynamics)
# Count of total number of steps
total_n_steps = 0
while True:
obs = observation['observation']
g = observation['desired_goal']
qpos = observation['sim_state'].qpos
qvel = observation['sim_state'].qvel
# Get action from the controller
ac, info = self.controller.act(observation)
if self.args.agent == 'rts':
assert self.controller.residual_dynamics_fn is None
if self.args.agent == 'mbpo' or self.args.agent == 'mbpo_knn' or self.args.agent == 'mbpo_gp':
assert self.controller.discrepancy_fn is None
# Get discrete action index
ac_ind = self.env.discrete_actions[tuple(ac)]
# Get the next observation
next_observation, rew, _, _ = self.env.step(ac)
# if np.array_equal(obs, next_observation['observation']):
# import ipdb
# ipdb.set_trace()
# print('ACTION', ac)
# print('VALUE PREDICTED', info['start_node_h'])
# print('COST', -rew)
if self.args.render:
self.env.render()
total_n_steps += 1
# Check if we reached the goal
if self.env.env._is_success(next_observation['achieved_goal'], g):
print('REACHED GOAL!')
break
# Get the next obs
obs_next = next_observation['observation']
# Get the sim next obs
set_sim_state_and_goal(self.planning_env, qpos.copy(), qvel.copy(),
g.copy())
next_observation_sim, _, _, _ = self.planning_env.step(ac)
obs_sim_next = next_observation_sim['observation']
# Store transition
transition = [
obs.copy(),
g.copy(), ac_ind,
qpos.copy(),
qvel.copy(),
obs_next.copy(),
obs_sim_next.copy()
]
dynamics_losses = []
# RTS
if self.args.agent == 'rts' and self._check_dynamics_transition(
transition):
# print('DISCREPANCY IN DYNAMICS')
self.memory.store_real_world_transition(transition)
# # Fit model
self._update_discrepancy_model()
# MBPO
elif self.args.agent == 'mbpo' or self.args.agent == 'mbpo_knn' or self.args.agent == 'mbpo_gp':
self.memory.store_real_world_transition(transition)
# Update the dynamics
if self.args.agent == 'mbpo':
for _ in range(self.args.n_online_planning_updates):
# Update dynamics
loss = self._update_batch_residual_dynamics()
dynamics_losses.append(loss.item())
else:
loss = self._update_residual_dynamics()
dynamics_losses.append(loss)
# # Plan in the model
value_loss = self.plan_online_in_model(
n_planning_updates=self.args.n_online_planning_updates,
initial_observation=copy.deepcopy(observation))
# Log
logger.record_tabular('n_steps', total_n_steps)
if self.args.agent == 'mbpo' or self.args.agent == 'mbpo_knn' or self.args.agent == 'mbpo_gp':
logger.record_tabular('dynamics loss',
np.mean(dynamics_losses))
logger.record_tabular('residual_loss', value_loss)
# logger.dump_tabular()
# Move to next iteration
observation = copy.deepcopy(next_observation)
if max_timesteps and total_n_steps >= max_timesteps:
break
return total_n_steps
def learn(self):
"""
train the network
"""
logger.info("Training..")
n_steps = 0
best_success_rate = 0.
# start to collect samples
for epoch in range(self.args.n_epochs):
actor_losses = []
critic_losses = []
switch_losses = []
for _ in range(self.args.n_cycles):
mb_obs, mb_ag, mb_g, mb_actions, mb_switch_actions = [], [], [], [], []
for _ in range(self.args.num_rollouts_per_mpi):
# reset the rollouts
ep_obs, ep_ag, ep_g, ep_actions, ep_switch_actions = [], [], [], [], []
# reset the environment
observation = self.env.reset()
obs = observation['observation']
ag = observation['achieved_goal']
g = observation['desired_goal']
# start to collect samples
for _ in range(self.env_params['max_timesteps']):
n_steps += 1
with torch.no_grad():
input_tensor = self._preproc_inputs(obs, g)
pi = self.actor_network(input_tensor)
_, switch_actions_q_values = self.critic_switch_network(
input_tensor, pi)
switch_action = self._select_switch_actions(
switch_actions_q_values)
# feed the actions into the environment
if switch_action == 0:
# Hardcoded action
action = self.controller.act(observation)
else:
# Learned policy action
action = self._select_actions(pi)
observation_new, _, _, info = self.env.step(action)
obs_new = observation_new['observation']
ag_new = observation_new['achieved_goal']
# append rollouts
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
ep_g.append(g.copy())
ep_actions.append(action.copy())
ep_switch_actions.append(switch_action)
# re-assign the observation
obs = obs_new
ag = ag_new
observation = observation_new
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
mb_obs.append(ep_obs)
mb_ag.append(ep_ag)
mb_g.append(ep_g)
mb_actions.append(ep_actions)
mb_switch_actions.append(ep_switch_actions)
# convert them into arrays
mb_obs = np.array(mb_obs)
mb_ag = np.array(mb_ag)
mb_g = np.array(mb_g)
mb_actions = np.array(mb_actions)
mb_switch_actions = np.array(mb_switch_actions)
# store the episodes
self.buffer.store_episode(
[mb_obs, mb_ag, mb_g, mb_actions, mb_switch_actions])
self._update_normalizer(
[mb_obs, mb_ag, mb_g, mb_actions, mb_switch_actions])
for _ in range(self.args.n_batches):
# train the network
critic_loss, actor_loss, switch_loss = self._update_network(
)
actor_losses.append(actor_loss.detach().numpy())
critic_losses.append(critic_loss.detach().numpy())
switch_losses.append(switch_loss.detach().numpy())
# soft update
self._soft_update_target_network(self.actor_target_network,
self.actor_network)
self._soft_update_target_network(
self.critic_switch_target_network,
self.critic_switch_network)
# start to do the evaluation
success_rate, prop_hardcoded = self._eval_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print(
'[{}] epoch is: {}, Num steps: {}, eval success rate is: {:.3f}'
.format(datetime.now(), epoch, n_steps, success_rate))
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_steps', n_steps)
logger.record_tabular('success_rate', success_rate)
logger.record_tabular('prop_hardcoded', prop_hardcoded)
logger.record_tabular('actor_loss', np.mean(actor_losses))
logger.record_tabular('critic_loss', np.mean(critic_losses))
logger.record_tabular('switch_loss', np.mean(switch_losses))
logger.dump_tabular()
if success_rate > best_success_rate:
logger.info("Better success rate... Saving policy")
torch.save([
self.o_norm.mean, self.o_norm.std, self.g_norm.mean,
self.g_norm.std,
self.actor_network.state_dict()
], self.model_path + '/model.pt')
best_success_rate = success_rate
def learn_online_in_real_world(self, max_timesteps=None):
# If any pre-existing model is given, load it
if self.args.load_dir:
self.load()
# Reset the environment
observation = self.env.reset()
# Configure heuristic for controller
self.controller.reconfigure_heuristic(
lambda obs: get_state_value_residual(obs, self.preproc_inputs, self
.state_value_residual))
# Configure dynamics for controller
self.controller.reconfigure_residual_dynamics(
self.get_residual_dynamics)
# Count total number of steps
total_n_steps = 0
while True:
obs = observation['observation']
g = observation['desired_goal']
qpos = observation['sim_state'].qpos
qvel = observation['sim_state'].qvel
# Get action from controller
ac, info = self.controller.act(observation)
# Get discrete action index
ac_ind = self.env.discrete_actions[tuple(ac)]
# Get next observation
next_observation, rew, _, _ = self.env.step(ac)
# Increment counter
total_n_steps += 1
if self.env.env._is_success(next_observation['achieved_goal'], g):
print('REACHED GOAL!')
break
if self.args.render:
self.env.render()
# Get next obs
obs_next = next_observation['observation']
# GEt sim next obs
set_sim_state_and_goal(self.planning_env, qpos.copy(), qvel.copy(),
g.copy())
next_observation_sim, _, _, _ = self.planning_env.step(ac)
obs_sim_next = next_observation_sim['observation']
# Store transition in real world memory
transition = [
obs.copy(),
g.copy(), ac_ind,
qpos.copy(),
qvel.copy(),
obs_next.copy(),
obs_sim_next.copy()
]
self.memory.store_real_world_transition(transition)
# Update the dynamics
dynamics_losses = []
for _ in range(self.args.n_online_planning_updates):
# Update dynamics
loss = self._update_residual_dynamics()
dynamics_losses.append(loss.item())
# Update state value residual
value_loss = self.plan_online_in_model(
self.args.n_online_planning_updates,
initial_observation=copy.deepcopy(observation))
# log
logger.record_tabular('n_steps', total_n_steps)
logger.record_tabular('dynamics_loss', np.mean(dynamics_losses))
logger.record_tabular('residual_loss', value_loss)
logger.dump_tabular()
# Move to next iteration
observation = copy.deepcopy(next_observation)
if max_timesteps and total_n_steps >= max_timesteps:
break
return total_n_steps
def learn(self):
logger.info("Training")
# ILC loop
# 1. Train the model on real environment transitions
# 2. Plan in the model to get the optimal policy, and the direction of improvement
# 3. Do line search on the real environment to find the right step size
initial_residual_parameters = copy.deepcopy(self.residual.state_dict())
for epoch in range(self.args.n_epochs):
# 0. Fix start and goals
self.populate_sim_states_and_goals()
# 1. Plan in the model to get the optimal policy
logger.info("Improving policy in the model")
residual_losses = []
for _ in range(self.args.n_cycles):
# Collect trajectories
self.controller.reconfigure_heuristic(self.get_residual)
self.controller.reconfigure_dynamics(
self.get_dynamics_residual)
self.collect_trajectories(
self.args.num_rollouts_per_mpi)
# Update residual
logger.info("Updating")
for _ in range(self.args.n_batches):
residual_loss = self._update_residual()
residual_losses.append(
residual_loss.detach().cpu().numpy())
logger.info('Residual Loss', residual_loss.item())
self._update_target_network(
self.residual_target, self.residual)
if not self.args.planning:
# Get the direction of improvement
logger.info("Computing direction of improvement")
final_residual_parameters = copy.deepcopy(
self.residual.state_dict())
gradient = {}
for key in initial_residual_parameters.keys():
gradient[key] = final_residual_parameters[key] - \
initial_residual_parameters[key]
# 2. Line search in the real world
logger.info("Line search in the real world")
logger.info("Evaluating initial policy in the real world")
initial_real_value_estimate = self.evaluate_real_world(
initial_residual_parameters)
logger.info("Initial cost-to-go", initial_real_value_estimate)
alpha = 1.0
while True:
logger.info("Alpha", alpha)
current_residual_parameters = {}
for key in initial_residual_parameters.keys():
current_residual_parameters[key] = initial_residual_parameters[key] + \
alpha * gradient[key]
current_real_value_estimate = self.evaluate_real_world(
current_residual_parameters)
logger.info("Current cost-to-go",
current_real_value_estimate)
if current_real_value_estimate < initial_real_value_estimate:
# Cost to go decreased - found an alpha
logger.info("Initial cost-to-go", initial_real_value_estimate,
"Final cost-to-go", current_real_value_estimate)
initial_real_value_estimate = current_real_value_estimate
initial_residual_parameters = copy.deepcopy(
current_residual_parameters)
break
else:
# Decrease alpha
alpha *= 0.5
if alpha < self.args.alpha_threshold:
# If alpha is really really small
# Don't update the residual
logger.info(
"Alpha really small. Not updating residual")
logger.info("Best cost-to-go so far",
initial_real_value_estimate)
break
# Assign chosen residual parameters for the residual
self.residual.load_state_dict(initial_residual_parameters)
self.residual_target.load_state_dict(
initial_residual_parameters)
logger.info("Evaluating")
success_rate = self.eval_agent()
if not self.args.planning:
# 3. Train model on real world transitions collected so far
logger.info("Training model residual using real world samples")
model_losses = []
for _ in range(self.args.n_model_batches):
model_loss = self._update_model()
model_losses.append(model_loss.detach().cpu().numpy())
logger.info('Model Loss', model_loss.item())
else:
model_losses = [0]
if MPI.COMM_WORLD.Get_rank() == 0:
print('[{}] epoch is: {}, Num planning steps: {}, Num real steps: {}, eval success rate is: {:.3f}'.format(
datetime.now(), epoch, self.n_planning_steps, self.n_real_steps, success_rate))
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_planning_steps',
self.n_planning_steps)
logger.record_tabular('n_real_steps', self.n_real_steps)
logger.record_tabular('success_rate', success_rate)
logger.record_tabular(
'residual_loss', np.mean(residual_losses))
logger.record_tabular('model_loss', np.mean(model_losses))
# logger.record_tabular(
# 'cost-to-go', initial_real_value_estimate)
logger.dump_tabular()
def learn(self):
"""
train the network
"""
logger.info("Training..")
n_steps = 0
best_success_rate = 0.
prev_actor_losses = [0.0]
actor_losses = [0.0]
critic_losses = []
original_actor_lr = self.args.lr_actor
coin_flipping = False
# start to collect samples
for epoch in range(self.args.n_epochs):
# If residual, then account for burn-in period by monitoring the decrement in loss
if (epoch == 0
or abs(np.mean(actor_losses) - np.mean(prev_actor_losses))
> self.args.threshold):
# Do not update actor, just update critic
logger.info('Only training critic')
self.change_actor_lr(0.0)
coin_flipping = True
else:
# Update actor as well
self.change_actor_lr(original_actor_lr)
coin_flipping = False
prev_actor_losses = actor_losses
actor_losses = []
critic_losses = []
for _ in range(self.args.n_cycles):
mb_obs, mb_ag, mb_g, mb_actions, mb_f = [], [], [], [], []
for _ in range(self.args.num_rollouts_per_mpi):
# reset the rollouts
ep_obs, ep_ag, ep_g, ep_actions, ep_f = [], [], [], [], []
# reset the environment
observation = self.env.reset()
obs = observation['observation']
ag = observation['achieved_goal']
g = observation['desired_goal']
f = self.env.extract_features(obs, g)
# start to collect samples
for t in range(self.env_params['max_timesteps']):
n_steps += 1
with torch.no_grad():
input_tensor = self._preproc_inputs(obs, g)
pi = self.actor_network(input_tensor)
action = self._select_actions(pi, coin_flipping)
# feed the actions into the environment
observation_new, _, _, info = self.env.step(action)
obs_new = observation_new['observation']
ag_new = observation_new['achieved_goal']
# append rollouts
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
ep_g.append(g.copy())
ep_actions.append(action.copy())
ep_f.append(f.copy())
# re-assign the observation
obs = obs_new
ag = ag_new
f = self.env.extract_features(obs, g)
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
ep_f.append(f.copy())
mb_obs.append(ep_obs)
mb_ag.append(ep_ag)
mb_g.append(ep_g)
mb_actions.append(ep_actions)
mb_f.append(ep_f)
# convert them into arrays
mb_obs = np.array(mb_obs)
mb_ag = np.array(mb_ag)
mb_g = np.array(mb_g)
mb_actions = np.array(mb_actions)
mb_f = np.array(mb_f)
# store the episodes
self.buffer.store_episode(
[mb_obs, mb_ag, mb_g, mb_actions, mb_f])
self._update_normalizer(
[mb_obs, mb_ag, mb_g, mb_actions, mb_f])
for _ in range(self.args.n_batches):
# train the network
critic_loss, actor_loss = self._update_network()
actor_losses.append(actor_loss.detach().numpy())
critic_losses.append(critic_loss.detach().numpy())
# soft update
self._soft_update_target_network(self.actor_target_network,
self.actor_network)
self._soft_update_target_network(self.critic_target_network,
self.critic_network)
# start to do the evaluation
success_rate = self._eval_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print(
'[{}] epoch is: {}, Num steps: {}, eval success rate is: {:.3f}'
.format(datetime.now(), epoch, n_steps, success_rate))
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_steps', n_steps)
logger.record_tabular('success_rate', success_rate)
logger.record_tabular('actor_loss', np.mean(actor_losses))
logger.record_tabular('critic_loss', np.mean(critic_losses))
logger.dump_tabular()
if success_rate > best_success_rate:
logger.info("Better success rate... Saving policy")
torch.save([
self.o_norm.mean, self.o_norm.std, self.g_norm.mean,
self.g_norm.std,
self.actor_network.state_dict()
], self.model_path + '/model.pt')
best_success_rate = success_rate
def learn_offline_in_model(self):
if not self.args.offline:
warnings.warn('SHOULD NOT BE USED ONLINE')
best_success_rate = 0.0
n_steps = 0
for epoch in range(self.args.n_epochs):
# Reset the environment
observation = self.env.reset()
obs = observation['observation']
g = observation['desired_goal']
for _ in range(self.env_params['offline_max_timesteps']):
# Get action
ac, info = self.controller.act(observation)
ac_ind = self.env.discrete_actions[tuple(ac)]
# Get the next observation and reward from the environment
observation_new, rew, _, _ = self.env.step(ac)
n_steps += 1
obs_new = observation_new['observation']
# Store the transition in memory
self.memory.store_real_world_transition(
[obs, g, ac_ind, obs_new], sim=False)
observation = copy.deepcopy(observation_new)
obs = obs_new.copy()
# Update state value residual from model rollouts
transitions = self.memory.sample_real_world_memory(
batch_size=self.args.n_cycles)
losses = []
model_losses = []
for i in range(self.args.n_cycles):
observation = {}
observation['observation'] = transitions['obs'][i].copy()
observation['achieved_goal'] = transitions['obs'][i][:3].copy()
observation['desired_goal'] = transitions['g'][i].copy()
# Collect model rollouts
self.collect_internal_model_trajectories(
num_rollouts=1,
rollout_length=self.env_params['offline_max_timesteps'],
initial_observations=[observation])
# Update state value residuals
for _ in range(self.args.n_batches):
state_value_residual_loss = self._update_state_value_residual(
).item()
losses.append(state_value_residual_loss)
self._update_target_network(self.state_value_target_residual,
self.state_value_residual)
# Update dynamics model
for _ in range(self.args.n_batches):
loss = self._update_learned_dynamics_model().item()
model_losses.append(loss)
# Evaluate agent in the model
mean_success_rate, mean_return = self.eval_agent_in_model()
# Check if this is a better residual
if mean_success_rate > best_success_rate:
best_success_rate = mean_success_rate
print('Best success rate so far', best_success_rate)
if self.args.save_dir is not None:
print('Saving residual')
self.save(epoch, best_success_rate)
# log
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_steps', n_steps)
logger.record_tabular('success_rate', mean_success_rate)
logger.record_tabular('return', mean_return)
logger.record_tabular('state_value_residual_loss', np.mean(losses))
logger.record_tabular('dynamics_loss', np.mean(model_losses))
logger.dump_tabular()
def learn(self):
"""
train the network
"""
logger.info("Training..")
n_psdp_iters = 0
n_steps = 0
best_success_rate = 0.
epoch = 0
success_rate = self._eval_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print(
'[{}] epoch is: {}, Num steps: {}, eval success rate is: {:.3f}'
.format(datetime.now(), epoch, n_steps, success_rate))
# start to collect samples
#assert self.args.n_cycles == self.T, "Number of cycles should be equal to horizon"
#actor_losses, prev_actor_losses = [0.], [0.]
#critic_losses, prev_critic_losses = [0.], [0.]
current_t = self.T
for epoch in range(self.args.n_epochs):
# TODO: Burn-in critic?
#prev_actor_losses = actor_losses
#prev_critic_losses = critic_losses
actor_losses = []
critic_losses = []
if epoch % 10 == 0:
current_t = current_t - 1
logger.info("Training residual policy at time step {}".format(
current_t))
# TODO: Update actors one at a time by monitoring corresponding critic loss
# Once the critic has been sufficiently trained, then we can start training the actor
# at that time-step before moving onto the next time-step
for _ in range(self.args.n_cycles):
# current_t -= 1
# if (current_t + 1) % 10 == 0:
# logger.info(
# "Training residual policy at time step {}".format(current_t))
# for current_t in range(self.T-1, -1, -1):
mb_obs, mb_ag, mb_g, mb_actions = [], [], [], []
for _ in range(self.args.num_rollouts_per_mpi):
# reset the rollouts
ep_obs, ep_ag, ep_g, ep_actions = [], [], [], []
# reset the environment
observation = self.env.reset()
obs = observation['observation']
ag = observation['achieved_goal']
g = observation['desired_goal']
# start to collect samples
for t in range(self.env_params['max_timesteps']):
n_steps += 1
with torch.no_grad():
input_tensor = self._preproc_inputs(obs, g)
if t == current_t:
# Use untrained residual policy
pi = self.actor_networks[t](input_tensor)
action = self._select_actions(pi)
# elif t > current_t:
else:
# Use current trained policy
# If it has not been trained, it will predict zeros as
# a result of our initialization
pi = self.actor_networks[t](input_tensor)
action = pi.cpu().numpy().squeeze()
# feed the actions into the environment
observation_new, _, _, info = self.env.step(action)
obs_new = observation_new['observation']
ag_new = observation_new['achieved_goal']
# append rollouts
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
ep_g.append(g.copy())
ep_actions.append(action.copy())
# re-assign the observation
obs = obs_new
ag = ag_new
ep_obs.append(obs.copy())
ep_ag.append(ag.copy())
mb_obs.append(ep_obs)
mb_ag.append(ep_ag)
mb_g.append(ep_g)
mb_actions.append(ep_actions)
# convert them into arrays
mb_obs = np.array(mb_obs)
mb_ag = np.array(mb_ag)
mb_g = np.array(mb_g)
mb_actions = np.array(mb_actions)
# store the episodes
self.buffer.store_episode([mb_obs, mb_ag, mb_g, mb_actions])
self._update_normalizer([mb_obs, mb_ag, mb_g, mb_actions],
current_t)
for _ in range(self.args.n_batches):
# train the network
critic_loss, actor_loss = self._update_network(current_t)
critic_losses.append(critic_loss.detach().numpy())
actor_losses.append(actor_loss.detach().numpy())
# soft update
# self._soft_update_target_network(
# self.actor_target_networks[current_t], self.actor_networks[current_t])
# FIX: No target network updates
# self._soft_update_target_network(
# self.critic_target_network, self.critic_network)
# self._hard_update_target_network(
# self.critic_target_network, self.critic_network)
# start to do the evaluation
success_rate = self._eval_agent()
if MPI.COMM_WORLD.Get_rank() == 0:
print(
'[{}] epoch is: {}, Current time step : {}, Num steps: {}, eval success rate is: {:.3f}'
.format(datetime.now(), epoch, current_t, n_steps,
success_rate))
logger.record_tabular('epoch', epoch)
logger.record_tabular('n_steps', n_steps)
logger.record_tabular('success_rate', success_rate)
logger.record_tabular('actor_loss', np.mean(actor_losses))
logger.record_tabular('critic_loss', np.mean(critic_losses))
logger.dump_tabular()
if success_rate > best_success_rate:
logger.info("Better success rate... Saving policy")
# torch.save([self.o_norm.mean, self.o_norm.std, self.g_norm.mean, self.g_norm.std, self.actor_network.state_dict()],
# self.model_path + '/model.pt')
torch.save([
self.o_norm.mean, self.o_norm.std, self.g_norm.mean,
self.g_norm.std
] + [
self.actor_networks[t].state_dict()
for t in range(self.T)
], self.model_path + '/model.pt')
best_success_rate = success_rate
