
    ni                        S r SSKJr  SSKJr  SSKJr  SSKrSSKrSSKrSSKrSSK	J
s  Jr  SSKJr  SSKJr  SSKJr  S	 rS
 rS rS r\S:X  a  \R.                  R0                  R2                  r\R.                  R0                  R5                  SSS5        \R.                  R0                  R5                  S\R
                  R7                  5       R9                  S5      S5        \R.                  R0                  R5                  SSS5        \R.                  R0                  R;                  SSS5        \R.                  R=                  5         gg)zScript to train a batch reinforcement learning algorithm.

Command line:

  python3 -m agents.scripts.train --logdir=/path/to/logdir --config=pendulum
    )absolute_import)division)print_functionN)tools)configs)utilityc                    [        U R                  [        5      (       a!  [        R                  " U R                  5      nOU R                  5       nU R
                  (       a)  [        R                  R                  XR
                  5      n[        R                  R                  U5      n[        R                  R                  U5      n[        R                  R                  U5      nU$ )zConstructor for an instance of the environment.

Args:
  config: Object providing configurations via attributes.

Returns:
  Wrapped OpenAI Gym environment.
)
isinstanceenvstrgymmake
max_lengthr   wrappersLimitDurationRangeNormalize
ClipActionConvertTo32Bit)configr   s     e/home/james-whalen/.local/lib/python3.13/site-packages/pybullet_envs/minitaur/agents/scripts/train.py_create_environmentr   %   s     

C  
((6::
C
**,C
..
&
&s,=,=
>C%%c*#!!#&#%%c*#	*    c                    [         R                  " XR                  U R                  U R                  U R
                  5      nUR                  SU R                  U R                  U R                  USUS-  SU R                  S0S9	  UR                  SU R                  U R                  U R                  UUUS-  SU-  U R                  S0S9	  U$ )	aI  Create and configure a training loop with training and evaluation phases.

Args:
  graph: Object providing graph elements via attributes.
  logdir: Log directory for storing checkpoints and summaries.
  train_steps: Number of training steps per epoch.
  eval_steps: Number of evaluation steps per epoch.

Returns:
  Loop object.
trainN   T)report_every	log_everycheckpoint_everyfeedeval
   F)r   Loopstep
should_log	do_reportforce_reset	add_phasedonescoresummaryis_training)graphlogdirtrain_steps
eval_stepsloops        r   _define_loopr1   :   s     
FJJ(8(8%//5K\K\	]$.."&!+"&(($/  1 ..(%?"$z/((%0  2 
+r   c              #   l  ^ #    [         R                  " 5         T R                     [        R                  " [
        R                  T R                  T 5      T l        [        [         R                  T R                  5      T l
        [        [         R                  T R                  5      T l        SSS5        T R                  T R                  -  (       a  [         R                  R                  S5        [         R                   " S5         [
        R"                  " U 4S jT R                  U5      n[
        R$                  " UT R&                  T 5      n[)        UT R*                  T R                  T R,                  -  T R.                  T R,                  -  5      n[1        T R2                  T R                  -  T R                  T R.                  -   -  5      nSSS5        [
        R4                  " SS9n[         R6                  " SS9nSUR8                  l        [         R<                  " US	9 n[
        R>                  " XT R*                  5        WRA                  XW5       H  n	U	v   M	     SSS5        WRC                  5         g! , (       d  f       GN= f! , (       d  f       N= f! , (       d  f       NB= f7f)
aa  Training and evaluation entry point yielding scores.

Resolves some configuration attributes, creates environments, graph, and
training loop. By default, assigns all operations to the CPU.

Args:
  config: Object providing configurations via attributes.
  env_processes: Whether to step environments in separate processes.

Yields:
  Evaluation scores.
Nz3Number of agents should divide episodes per update.z/cpu:0c                     > [        T 5      $ )N)r   r   s   r   <lambda>train.<locals>.<lambda>q   s    1DV1Lr   )z.*_temporary/.*)excludeT)allow_soft_placementr4   )"tfreset_default_graphunlocked	functoolspartialr   define_networknetworkgetattrr   policy_optimizervalue_optimizerupdate_every
num_agentsloggingwarndevicedefine_batch_envdefine_simulation_graph	algorithmr1   r-   r   eval_episodesintstepsdefine_saverConfigProtogpu_optionsallow_growthSessioninitialize_variablesrunclose)
r   env_processes	batch_envr,   r0   total_stepssaversess_configsessr)   s
   `         r   r   r   \   s     &&w'='=v~~vVFN%bhh0G0GHF$RXXv/E/EFF  6,,,JJOOIJ	yy(()LfN_N_)68I++Iv7G7GPEv}}f.A.AFDUDU.U,,v/@/@@BDfllV%8%88**V-A-AAC DK  

'<
=%D9+)-+&	zz%  fmm<${3k 4 & ///   &%sP   "J4BJ -AJ4CJAJ4(?J#'J4 
J
J4
J J4#
J1-J4c           	         [         R                  " 5         [        R                  (       d  [	        S5      e[        R
                  =(       aw    [        R                  R                  [        R                  R                  [        R
                  SR                  [        R                  [        R                  5      5      5      n [         R                  " U5      n['        U[        R(                  5       H1  n[*        R,                  R/                  SR                  U5      5        M3     g! [         aP    [        R                  " [!        ["        [        R                  5      " 5       5      n[         R$                  " X!5      n Nf = f)z4Create or load configuration and launch the trainer.z!You must specify a configuration.z{}-{}z	Score {}.N)r   set_up_loggingFLAGSr   KeyErrorr-   ospath
expanduserjoinformat	timestampload_configIOErrorr   AttrDictr@   r   save_configr   rV   r9   rE   info)_r-   r   r)   s       r   mainrl      s    		
6
77<< QBGG..ggll5<<!NOQ&1  (F VU001eJJOOK&&u-. 2 
 1^^GGU\\:<=F  0F1s   D+ +AFF__main__r-   zBase directory to store logs.re   z%Y%m%dT%H%M%SzSub directory to store logs.r   zConfiguration to execute.rV   Tz>Step environments in separate processes to circumvent the GIL.)__doc__
__future__r   r   r   datetimer<   r`   r   tf.compat.v1compatv1r9   pybullet_envs.minitaur.agentsr   %pybullet_envs.minitaur.agents.scriptsr   r   r   r1   r   rl   __name__appflagsr^   DEFINE_stringnowstrftimeDEFINE_booleanrT    r   r   <module>r~      s   '  %   	 
   / 9 9*D%P/  z
&&,,

%&&,,Xt-LM&&,,[%..224==oN;= &&,,Xt-HI&&,,ot^`&&**, r   