
    nih                     l    S r SSKJr  SSKJr  SSKJr  SSKJs  Jr  SSK	J
r
  SSKJr   " S S	\5      rg)
z:An agent that can restore and run a policy learned by PPO.    )absolute_import)division)print_functionN)	normalize)utilityc                   6    \ rS rSrSrS rS rS rS rS r	Sr
g	)
SimplePPOPolicy   a  A simple PPO policy that is independent to the PPO infrastructure.

This class restores the policy network from a tensorflow checkpoint that was
learned from PPO training. The purpose of this class is to conveniently
visualize a learned policy or deploy the learned policy on real robots without
need to change the PPO evaluation infrastructure:
https://cs.corp.google.com/piper///depot/google3/robotics/reinforcement_learning/agents/scripts/visualize.py.
c                 h   X l         Xl        [        UR                  R                  5      n[        UR
                  R                  5      n[        R                  " [        R                  S U/SS9U l	        [        R                  " U R                  S   SSSSS9U l        U R                  UUUUUS9  g )	NInput)namer   T   normalize_observ)centerscaleclipr   )policy_layersvalue_layersaction_size
checkpoint)envsesslenobservation_spacelowaction_spacetfplaceholderfloat32observation_placeholderr   StreamingNormalize_observ_filter_restore_policy)	selfr   r   networkr   r   r   observation_sizer   s	            f/home/james-whalen/.local/lib/python3.13/site-packages/pybullet_envs/minitaur/envs/simple_ppo_agent.py__init__SimplePPOPolicy.__init__   s    HI300445c&&**+K#%>>"**tEU>V7>$@D #66t7S7STU7V>B=A<=<N	PD
 	'4&2%0$.	  0    c           
         U R                   R                  U R                  5      n[        R                  " S5         U" UUUS9U l        SSS5        [        R                  " S5         [        R                  " U R
                  R                  S[        R                  5      S5      U l	        U R                  R                  U R                  R                  5        SSS5        [        R                  " S5         [        R                  R                  U R
                  USS2S4   [        R                  " S5      U R                  [        R                  SS	9u  u  n  pXpl        U R                  R#                  U	5      U l        SSS5        [&        R(                  " S
S9n
U
R+                  U R                  U5        g! , (       d  f       GN|= f! , (       d  f       N= f! , (       d  f       Nb= f)a  Restore the PPO policy from a TensorFlow checkpoint.

Args:
  network: The neural network definition.
  policy_layers: A tuple specify the number of layers and number of neurons
    of each layer for the policy network.
  value_layers: A tuple specify the number of layers and number of neurons
    of each layer for the value network.
  action_size: The dimension of the action space.
  checkpoint: The checkpoint path.
znetwork/rnn)r   r   r   N	temporary   Fr%   T)swap_memory)ztemporary/.*)exclude)r"   	transformr    r   variable_scoper%   Variable
zero_stater   
last_stater   runinitializernndynamic_rnnonesmean_actionassignupdate_stater   define_saverrestore)r$   r%   r   r   r   r   observr:   _	new_statesavers              r'   r#   SimplePPOPolicy._restore_policy(   sb      **4+G+GHF			=	)=*6)46dl 
*
 
		;	'DLL$;$;Arzz$JERdo
iimmDOO//0 
( 
		9	%')uu'8'89?49;9=9;EI (9 (K${Aq %//00;d 
&   );<E	MM$))Z() 
*	)
 
(	' 
&	%s%   F?(A4G:BG"?
G
G"
G0c                     U R                  U5      nU R                  R                  U R                  U R                  /U R
                  U0S9u  p4U R                  U5      nUS S 2S4   $ )N)	feed_dictr   )_normalize_observr   r5   r:   r<   r    _denormalize_action)r$   observationnormalized_observationnormalized_actionr@   actions         r'   
get_actionSimplePPOPolicy.get_actionK   ss    !33K@99==			4,,-//1GH ) J %%&78F!Q$<r*   c                     U R                   R                  R                  nU R                   R                  R                  nUS-   S-  X2-
  -  U-   nU$ )Nr-      )r   r   r   high)r$   rK   min_max_s       r'   rG   #SimplePPOPolicy._denormalize_actionS   sK    88  $$D88  %%DqjA-4FMr*   c                     U R                   R                  R                  nU R                   R                  R                  nSX-
  -  X2-
  -  S-
  nU$ )NrO   r-   )r   r   r   rP   )r$   r?   rQ   rR   s       r'   rF   !SimplePPOPolicy._normalize_observY   sI    88%%))D88%%**D&- DK014FMr*   )r"   r   r4   r:   r%   r    r   r<   N)__name__
__module____qualname____firstlineno____doc__r(   r#   rL   rG   rF   __static_attributes__ r*   r'   r	   r	      s!    0$!)Fr*   r	   )rZ   
__future__r   r   r   tf.compat.v1compatv1r   pybullet_envs.agents.ppor   pybullet_envs.agentsr   objectr	   r\   r*   r'   <module>rd      s+    @ &  %   . (Qf Qr*   