ni SrSSKJr SSKJr SSKJr SSKrSSKrSSKrSSKJ r SSK J s J r SSKJr SrSrSS jrS rSS jrSS jrS rSrg)z6Utilities for using reinforcement learning algorithms.)absolute_import)division)print_functionN)toolscn[R"SS[RSS9n[R"[RSS9n[R"[RSS9n[R"[RSS9n[R"[RS S9nU"XXEU5n[ R "XXW5upn S n [RRU R[ R"555 [ R"[55$) aDefine the algortihm and environment interaction. Args: batch_env: In-graph environments object. algo_cls: Constructor of a batch algorithm. config: Configuration object for the algorithm. Returns: Object providing graph elements via attributes. rF global_step)dtypename is_training)r should_log do_report force_resetz&Graph contains {} trainable variables.) tfVariableint32 placeholderboolrsimulatelogginginfoformat count_weightsAttrDictlocals) batch_envalgo_clsconfigstepr r r ralgodonescoresummarymessages g/home/james-whalen/.local/lib/python3.13/site-packages/pybullet_envs/minitaur/agents/scripts/utility.pydefine_simulation_graphr%s QRXXM B$rww];+~~bggL9*nnRWW;7)rww];+ );F C$ Q$w 4'**//'..!4!4!678  !!c[R"S5 U(a9[U5Vs/sH"n[RR U5PM$ nnO[U5Vs/sH o0"5PM nn[R "XB(+S9n[R"U5nSSS5 U$s snfs snf!,(df  W$=f)a Create environments and apply all desired wrappers. Args: constructor: Constructor of an OpenAI gym environment. num_agents: Number of environments to combine in the batch. env_processes: Whether to step environment in external processes. Returns: In-graph environments object. environments)blockingN)rvariable_scoperangerwrappersExternalProcessBatchEnvInGraphBatchEnv) constructor num_agents env_processes_envsrs r$define_batch_envr57s (CHCT UCTaenn,,[9CTd Ud%*:%6 7%6km%6d 7t.?@I%%i0I )  V 7 )( s(B:)B0B:%B551B:0 B:: C cF^/nU=(d /nUVs/sHn[R"U5PM nn[R"5H0m[ U4SjU55(aMUR T5 M2 [R RUSS9nU$s snf)zCreate a saver for the variables we want to checkpoint. Args: exclude: List of regexes to match variable names to exclude. Returns: Saver object. c3X># UHoRTR5v M! g7fN)matchr ).0regexvariables r$ define_saver..Ys ;7%;;x}} % %7s'*)keep_checkpoint_every_n_hours)recompilerglobal_variablesanyappendtrainSaver)exclude variablesr;saverr<s @r$ define_saverrKLs) Mr',3 4G5RZZ G' 4%%'h ;7 ;;; X( ((..!. D% , 5s Bc[RRRURS9n[R "UR S5nU"URURUUUS9nU$)aConstructor for the recurrent cell for the algorithm. Args: constructor: Callable returning the network as RNNCell. config: Object providing configurations via attributes. action_size: Integer indicating the amount of action dimensions. Returns: Created recurrent cell object. )factorg|=)mean_weights_initializerlogstd_initializer) rcontriblayersvariance_scaling_initializerinit_mean_factorrandom_normal_initializer init_logstd policy_layers value_layers)r0r action_sizerNrOnetworks r$define_networkrZ`sr!jj//LL  $ $M&33F4F4FN ,,++#1I+=  ?' .r&c0UR[R"[R"5[R"555 U(aU(dU(d [ S5eU(a[R RU5nU(a[RRX#5nU(d$U(aUR(a URnU(aUSLa Sn[U5eU(aURX5 ggg)aInitialize or restore variables from a checkpoint if available. Args: sess: Session to initialize variables in. saver: Saver to restore variables. logdir: Directory to search for checkpoints. checkpoint: Specify what checkpoint name to use; defaults to most recent. resume: Whether to expect recovering a checkpoint or starting a new run. Raises: ValueError: If resume expected but no log directory specified. RuntimeError: If no resume expected but a checkpoint was found. z.Need to specify logdir to resume a checkpoint.Fz4Found unexpected checkpoint when starting a new run.N)runrgrouplocal_variables_initializerglobal_variables_initializer ValueErrorrFget_checkpoint_stateospathjoinmodel_checkpoint_path RuntimeErrorrestore)sessrJlogdir checkpointresumestater#s r$initialize_variablesrmvs((288B224b6U6U6W XY Vz E FF HH ) )& 1E77<<3j %E$?$?..jfoFg  !! mmD% r&clU(aUR XlSSS5 Sn[RR UR UR55 [R RUR5 [RRURS5n[R RUS5n[R"XSS9 SSS5 U$Sn[RR U5 U$!,(df  N=f!,(df  U$=f)aASave a new configuration by name. If a logging directory is specified, is will be created and the configuration will be stored there. Otherwise, a log message will be printed. Args: config: Configuration object. logdir: Location for writing summaries and checkpoints if specified. Returns: Configuration object. Nz:Start a new run and write summaries and checkpoints to {}. config.yamlwF)default_flow_stylezcStart a new run without storing summaries and checkpoints since no logging directory was specified.)unlockedrirrrrgfileMakeDirsrbrcrdGFileyamldump)rrir# config_pathfile_s r$ save_configrzs m JGJJOOGNN6==12HHfmm$'',,v}}m277<< >+ BHHOOK88/G '  xx+s+u YYu F, C'**//'../0 - ,+s C C+c[RR[RR5 S[R"S5lg)z Configure the TensorFlow logger.F tensorflowN)rr set_verbosityINFO getLogger propagater&r$set_up_loggingrs/**2::??+.3'L!+r&r8)NN)__doc__ __future__rrrrrbrA ruamel.yamlrv tf.compat.v1compatv1rpybullet_envs.minitaur.agentsrr%r5rKrZrmrzrrrr&r$rsP=&% /"2*(,&<:04r&