<s> from rllab . algos . base import RLAlgorithm <EOL> from rllab . misc . overrides import overrides <EOL> from rllab . misc import special <EOL> from rllab . misc import ext <EOL> from rllab . sampler import parallel_sampler <EOL> from rllab . plotter import plotter <EOL> from functools import partial <EOL> import rllab . misc . logger as logger <EOL> import theano . tensor as TT <EOL> import cPickle as pickle <EOL> import numpy as np <EOL> import pyprind <EOL> import lasagne <EOL> def parse_update_method ( update_method , ** kwargs ) : <EOL> if update_method == '<STR_LIT>' : <EOL> return partial ( lasagne . updates . adam , ** ext . compact ( kwargs ) ) <EOL> elif update_method == '<STR_LIT>' : <EOL> return partial ( lasagne . updates . sgd , ** ext . compact ( kwargs ) ) <EOL> else : <EOL> raise NotImplementedError <EOL> class SimpleReplayPool ( object ) : <EOL> def __init__ ( <EOL> self , max_pool_size , observation_dim , action_dim ) : <EOL> self . _observation_dim = observation_dim <EOL> self . _action_dim = action_dim <EOL> self . _max_pool_size = max_pool_size <EOL> self . _observations = np . zeros ( <EOL> ( max_pool_size , observation_dim ) , <EOL> ) <EOL> self . _actions = np . zeros ( <EOL> ( max_pool_size , action_dim ) , <EOL> ) <EOL> self . _rewards = np . zeros ( max_pool_size ) <EOL> self . _terminals = np . zeros ( max_pool_size , dtype = '<STR_LIT>' ) <EOL> self . _bottom = <NUM_LIT:0> <EOL> self . _top = <NUM_LIT:0> <EOL> self . _size = <NUM_LIT:0> <EOL> def add_sample ( self , observation , action , reward , terminal ) : <EOL> self . _observations [ self . _top ] = observation <EOL> self . _actions [ self . _top ] = action <EOL> self . _rewards [ self . _top ] = reward <EOL> self . _terminals [ self . _top ] = terminal <EOL> self . _top = ( self . _top + <NUM_LIT:1> ) % self . _max_pool_size <EOL> if self . _size >= self . _max_pool_size : <EOL> self . _bottom = ( self . _bottom + <NUM_LIT:1> ) % self . _max_pool_size <EOL> else : <EOL> self . _size += <NUM_LIT:1> <EOL> def random_batch ( self , batch_size ) : <EOL> assert self . _size > batch_size <EOL> indices = np . zeros ( batch_size , dtype = '<STR_LIT>' ) <EOL> transition_indices = np . zeros ( batch_size , dtype = '<STR_LIT>' ) <EOL> count = <NUM_LIT:0> <EOL> while count < batch_size : <EOL> index = np . random . randint ( self . _bottom , self . _bottom + self . _size ) % self . _max_pool_size <EOL> if index == self . _size - <NUM_LIT:1> and self . _size <= self . _max_pool_size : <EOL> continue <EOL> transition_index = ( index + <NUM_LIT:1> ) % self . _max_pool_size <EOL> indices [ count ] = index <EOL> transition_indices [ count ] = transition_index <EOL> count += <NUM_LIT:1> <EOL> return dict ( <EOL> observations = self . _observations [ indices ] , <EOL> actions = self . _actions [ indices ] , <EOL> rewards = self . _rewards [ indices ] , <EOL> terminals = self . _terminals [ indices ] , <EOL> next_observations = self . _observations [ transition_indices ] <EOL> ) <EOL> @ property <EOL> def size ( self ) : <EOL> return self . _size <EOL> class DDPG ( RLAlgorithm ) : <EOL> """<STR_LIT>""" <EOL> def __init__ ( <EOL> self , <EOL> env , <EOL> policy , <EOL> qf , <EOL> es , <EOL> batch_size = <NUM_LIT:32> , <EOL> n_epochs = <NUM_LIT:200> , <EOL> epoch_length = <NUM_LIT:1000> , <EOL> min_pool_size = <NUM_LIT> , <EOL> replay_pool_size = <NUM_LIT> , <EOL> discount = <NUM_LIT> , <EOL> max_path_length = <NUM_LIT> , <EOL> qf_weight_decay = <NUM_LIT:0.> , <EOL> qf_update_method = '<STR_LIT>' , <EOL> qf_learning_rate = <NUM_LIT> , <EOL> policy_weight_decay = <NUM_LIT:0> , <EOL> policy_update_method = '<STR_LIT>' , <EOL> policy_learning_rate = <NUM_LIT> , <EOL> eval_samples = <NUM_LIT> , <EOL> soft_target = True , <EOL> soft_target_tau = <NUM_LIT> , <EOL> n_updates_per_sample = <NUM_LIT:1> , <EOL> scale_reward = <NUM_LIT:1.0> , <EOL> include_horizon_terminal_transitions = False , <EOL> plot = False ) : <EOL> """<STR_LIT>""" <EOL> self . env = env <EOL> self . policy = policy <EOL> self . qf = qf <EOL> self . es = es <EOL> self . batch_size = batch_size <EOL> self . n_epochs = n_epochs <EOL> self . epoch_length = epoch_length <EOL> self . min_pool_size = min_pool_size <EOL> self . replay_pool_size = replay_pool_size <EOL> self . discount = discount <EOL> self . max_path_length = max_path_length <EOL> self . qf_weight_decay = qf_weight_decay <EOL> self . qf_update_method = parse_update_method ( <EOL> qf_update_method , <EOL> learning_rate = qf_learning_rate , <EOL> ) <EOL> self . qf_learning_rate = qf_learning_rate <EOL> self . policy_weight_decay = policy_weight_decay <EOL> self . policy_update_method = parse_update_method ( <EOL> policy_update_method , <EOL> learning_rate = policy_learning_rate , <EOL> ) <EOL> self . policy_learning_rate = policy_learning_rate <EOL> self . eval_samples = eval_samples <EOL> self . soft_target_tau = soft_target_tau <EOL> self . n_updates_per_sample = n_updates_per_sample <EOL> self . include_horizon_terminal_transitions = include_horizon_terminal_transitions <EOL> self . plot = plot <EOL> self . qf_loss_averages = [ ] <EOL> self . policy_surr_averages = [ ] <EOL> self . q_averages = [ ] <EOL> self . y_averages = [ ] <EOL> self . paths = [ ] <EOL> self . es_path_returns = [ ] <EOL> self . paths_samples_cnt = <NUM_LIT:0> <EOL> self . scale_reward = scale_reward <EOL> self . opt_info = None <EOL> def start_worker ( self ) : <EOL> parallel_sampler . populate_task ( self . env , self . policy ) <EOL> if self . plot : <EOL> plotter . init_plot ( self . env , self . policy ) <EOL> @ overrides <EOL> def train ( self ) : <EOL> pool = SimpleReplayPool ( <EOL> max_pool_size = self . replay_pool_size , <EOL> observation_dim = self . env . observation_space . flat_dim , <EOL> action_dim = self . env . action_space . flat_dim , <EOL> ) <EOL> self . start_worker ( ) <EOL> self . init_opt ( ) <EOL> itr = <NUM_LIT:0> <EOL> path_length = <NUM_LIT:0> <EOL> path_return = <NUM_LIT:0> <EOL> terminal = False <EOL> observation = self . env . reset ( ) <EOL> sample_policy = pickle . loads ( pickle . dumps ( self . policy ) ) <EOL> for epoch in xrange ( self . n_epochs ) : <EOL> logger . push_prefix ( '<STR_LIT>' % epoch ) <EOL> logger . log ( "<STR_LIT>" ) <EOL> for epoch_itr in pyprind . prog_bar ( xrange ( self . epoch_length ) ) : <EOL> if terminal : <EOL> observation = self . env . reset ( ) <EOL> self . es . reset ( ) <EOL> sample_policy . reset ( ) <EOL> self . es_path_returns . append ( path_return ) <EOL> path_length = <NUM_LIT:0> <EOL> path_return = <NUM_LIT:0> <EOL> action = self . es . get_action ( itr , observation , policy = sample_policy ) <EOL> next_observation , reward , terminal , _ = self . env . step ( action ) <EOL> path_length += <NUM_LIT:1> <EOL> path_return += reward <EOL> if not terminal and path_length >= self . max_path_length : <EOL> terminal = True <EOL> if self . include_horizon_terminal_transitions : <EOL> pool . add_sample ( observation , action , reward * self . scale_reward , terminal ) <EOL> else : <EOL> pool . add_sample ( observation , action , reward * self . scale_reward , terminal ) <EOL> observation = next_observation <EOL> if pool . size >= self . min_pool_size : <EOL> for update_itr in xrange ( self . n_updates_per_sample ) : <EOL> batch = pool . random_batch ( self . batch_size ) <EOL> self . do_training ( itr , batch ) <EOL> sample_policy . set_param_values ( self . policy . get_param_values ( ) ) <EOL> itr += <NUM_LIT:1> <EOL> logger . log ( "<STR_LIT>" ) <EOL> if pool . size >= self . min_pool_size : <EOL> self . evaluate ( epoch , pool ) <EOL> params = self . get_epoch_snapshot ( epoch ) <EOL> logger . save_itr_params ( epoch , params ) <EOL> logger . dump_tabular ( with_prefix = False ) <EOL> logger . pop_prefix ( ) <EOL> self . env . terminate ( ) <EOL> self . policy . terminate ( ) <EOL> def init_opt ( self ) : <EOL> target_policy = pickle . loads ( pickle . dumps ( self . policy ) ) <EOL> target_qf = pickle . loads ( pickle . dumps ( self . qf ) ) <EOL> obs = self . env . observation_space . new_tensor_variable ( <EOL> '<STR_LIT>' , <EOL> extra_dims = <NUM_LIT:1> , <EOL> ) <EOL> action = self . env . action_space . new_tensor_variable ( <EOL> '<STR_LIT:action>' , <EOL> extra_dims = <NUM_LIT:1> , <EOL> ) <EOL> yvar = TT . vector ( '<STR_LIT>' ) <EOL> qf_weight_decay_term = <NUM_LIT:0.5> * self . qf_weight_decay * sum ( [ TT . sum ( TT . square ( param ) ) for param in <EOL> self . qf . get_params ( regularizable = True ) ] ) <EOL> qval = self . qf . get_qval_sym ( obs , action ) <EOL> qf_loss = TT . mean ( TT . square ( yvar - qval ) ) <EOL> qf_reg_loss = qf_loss + qf_weight_decay_term <EOL> policy_weight_decay_term = <NUM_LIT:0.5> * self . policy_weight_decay * sum ( [ TT . sum ( TT . square ( param ) ) <EOL> for param in self . policy . get_params ( regularizable = True ) ] ) <EOL> policy_qval = self . qf . get_qval_sym ( <EOL> obs , self . policy . get_action_sym ( obs ) , <EOL> deterministic = True <EOL> ) <EOL> policy_surr = - TT . mean ( policy_qval ) <EOL> policy_reg_surr = policy_surr + policy_weight_decay_term <EOL> qf_updates = self . qf_update_method ( <EOL> qf_reg_loss , self . qf . get_params ( trainable = True ) ) <EOL> policy_updates = self . policy_update_method ( <EOL> policy_reg_surr , self . policy . get_params ( trainable = True ) ) <EOL> f_train_qf = ext . compile_function ( <EOL> inputs = [ yvar , obs , action ] , <EOL> outputs = [ qf_loss , qval ] , <EOL> updates = qf_updates <EOL> ) <EOL> f_train_policy = ext . compile_function ( <EOL> inputs = [ obs ] , <EOL> outputs = policy_surr , <EOL> updates = policy_updates <EOL> ) <EOL> self . opt_info = dict ( <EOL> f_train_qf = f_train_qf , <EOL> f_train_policy = f_train_policy , <EOL> target_qf = target_qf , <EOL> target_policy = target_policy , <EOL> ) <EOL> def do_training ( self , itr , batch ) : <EOL> obs , actions , rewards , next_obs , terminals = ext . extract ( <EOL> batch , <EOL> "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" , "<STR_LIT>" , <EOL> "<STR_LIT>" <EOL> ) <EOL> target_qf = self . opt_info [ "<STR_LIT>" ] <EOL> target_policy = self . opt_info [ "<STR_LIT>" ] <EOL> next_actions , _ = target_policy . get_actions ( next_obs ) <EOL> next_qvals = target_qf . get_qval ( next_obs , next_actions ) <EOL> ys = rewards + ( <NUM_LIT:1.> - terminals ) * self . discount * next_qvals <EOL> f_train_qf = self . opt_info [ "<STR_LIT>" ] <EOL> f_train_policy = self . opt_info [ "<STR_LIT>" ] <EOL> qf_loss , qval = f_train_qf ( ys , obs , actions ) <EOL> policy_surr = f_train_policy ( obs ) <EOL> target_policy . set_param_values ( <EOL> target_policy . get_param_values ( ) * ( <NUM_LIT:1.0> - self . soft_target_tau ) + <EOL> self . policy . get_param_values ( ) * self . soft_target_tau ) <EOL> target_qf . set_param_values ( <EOL> target_qf . get_param_values ( ) * ( <NUM_LIT:1.0> - self . soft_target_tau ) + <EOL> self . qf . get_param_values ( ) * self . soft_target_tau ) <EOL> self . qf_loss_averages . append ( qf_loss ) <EOL> self . policy_surr_averages . append ( policy_surr ) <EOL> self . q_averages . append ( qval ) <EOL> self . y_averages . append ( ys ) <EOL> def evaluate ( self , epoch , pool ) : <EOL> logger . log ( "<STR_LIT>" ) <EOL> paths = parallel_sampler . sample_paths ( <EOL> policy_params = self . policy . get_param_values ( ) , <EOL> max_samples = self . eval_samples , <EOL> max_path_length = self . max_path_length , <EOL> ) <EOL> average_discounted_return = np . mean ( <EOL> [ special . discount_return ( path [ "<STR_LIT>" ] , self . discount ) for path in paths ] <EOL> ) <EOL> returns = [ sum ( path [ "<STR_LIT>" ] ) for path in paths ] <EOL> all_qs = np . concatenate ( self . q_averages ) <EOL> all_ys = np . concatenate ( self . y_averages ) <EOL> average_q_loss = np . mean ( self . qf_loss_averages ) <EOL> average_policy_surr = np . mean ( self . policy_surr_averages ) <EOL> average_action = np . mean ( np . square ( np . concatenate ( <EOL> [ path [ "<STR_LIT>" ] for path in paths ] <EOL> ) ) ) <EOL> policy_reg_param_norm = np . linalg . norm ( <EOL> self . policy . get_param_values ( regularizable = True ) <EOL> ) <EOL> qfun_reg_param_norm = np . linalg . norm ( <EOL> self . qf . get_param_values ( regularizable = True ) <EOL> ) <EOL> logger . record_tabular ( '<STR_LIT>' , epoch ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . mean ( returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . std ( returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . max ( returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . min ( returns ) ) <EOL> if len ( self . es_path_returns ) > <NUM_LIT:0> : <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . mean ( self . es_path_returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . std ( self . es_path_returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . max ( self . es_path_returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . min ( self . es_path_returns ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> average_discounted_return ) <EOL> logger . record_tabular ( '<STR_LIT>' , average_q_loss ) <EOL> logger . record_tabular ( '<STR_LIT>' , average_policy_surr ) <EOL> logger . record_tabular ( '<STR_LIT>' , np . mean ( all_qs ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , np . mean ( np . abs ( all_qs ) ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , np . mean ( all_ys ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , np . mean ( np . abs ( all_ys ) ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> np . mean ( np . abs ( all_qs - all_ys ) ) ) <EOL> logger . record_tabular ( '<STR_LIT>' , average_action ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> policy_reg_param_norm ) <EOL> logger . record_tabular ( '<STR_LIT>' , <EOL> qfun_reg_param_norm ) <EOL> self . env . log_diagnostics ( paths ) <EOL> self . policy . log_diagnostics ( paths ) <EOL> self . qf_loss_averages = [ ] <EOL> self . policy_surr_averages = [ ] <EOL> self . q_averages = [ ] <EOL> self . y_averages = [ ] <EOL> self . es_path_returns = [ ] <EOL> def get_epoch_snapshot ( self , epoch ) : <EOL> return dict ( <EOL> env = self . env , <EOL> epoch = epoch , <EOL> qf = self . qf , <EOL> policy = self . policy , <EOL> target_qf = self . opt_info [ "<STR_LIT>" ] , <EOL> target_policy = self . opt_info [ "<STR_LIT>" ] , <EOL> es = self . es , <EOL> ) </s>
