&`i] ddlZddlZddlZddlZddlmZmZddlmZm Z m Z m Z m Z m Z mZmZmZddlZddlZddlZddlmZddlmZddlZddlmZddlmZm Z ddl!m"Z"ddl#m$Z$dd l%m&Z&dd l'm(Z(dd l)m*Z*dd l+m,Z,dd l-m.Z.m/Z/m0Z0m1Z1ddl2m3Z3m4Z4m5Z5ddl6m7Z7ddl8m9Z9m:Z:ddl;mZ>ddl?m@Z@mAZAmBZBddlCmDZDmEZEmFZFddlGmHZHddlImJZJddlKmLZLmMZMmNZNmOZOmPZPmQZQmRZRmSZSmTZTddlUmVZVe9\ZWZXZYe:\ZZZ[ej\e]Z^e.GddZ_e.GddeZ`e. d#dee ee eTfd ejafd!Zbe.dee efd ejcj fd"ZddS)$N)ABCMetaabstractmethod) AnyCallable CollectionDictListOptionalTupleTypeUnion)Box)version)DEPRECATED_VALUEdeprecation_warning) ActorHandle)ActionDistribution) ModelCatalog)ModelV2) SampleBatch)ViewRequirement) OldAPIStackOverrideToImplementCustomLogic5OverrideToImplementCustomLogic_CallToSuperRecommended is_overridden)CHECKPOINT_VERSIONget_checkpoint_infotry_import_msgpack) Exploration) try_import_tftry_import_torch) from_config)convert_to_numpy)deserialize_typespace_from_dict space_to_dict)get_base_struct_from_spaceget_dummy_batch_for_spaceunbatch) get_np_dtype)get_tf_eager_cls_if_necessary) AgentIDAlgorithmConfigDictModelGradients ModelWeightsPolicyID PolicyStateTTensorStructType TensorType) CheckpointceZdZdZ d dZddZdeeeffdZ e deeefddfd Z defd Z e d eddfd Z dS) PolicySpecaQA policy spec used in the "config.multiagent.policies" specification dict. As values (keys are the policy IDs (str)). E.g.: config: multiagent: policies: { "pol1": PolicySpec(None, Box, Discrete(2), {"lr": 0.0001}), "pol2": PolicySpec(config={"lr": 0.001}), } Nc>||_||_||_||_dSN policy_classobservation_space action_spaceconfig)selfr;r<r=r>s k/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/policy/policy.py__init__zPolicySpec.__init__`s- )"3) otherc|j|jko/|j|jko|j|jko|j|jkSr9r:)r?rCs r@__eq__zPolicySpec.__eq__psN  !3 3 ,&%*AA ,!U%77 , u|+  rBreturnc6|j|j|j|jfS)z.Returns the state of a `PolicyDict` as a dict.r:r?s r@ get_statezPolicySpec.get_statexs%    "   K   rBstatecVt}|j||S)z#Builds a `PolicySpec` from a state.)r7__dict__update)clsrJ policy_specs r@ from_statezPolicySpec.from_states*!ll ##E***rBcddlm}||j}|*td|jd|j}|t |jt |j|jdS)Nr)get_policy_class_namez-Can not figure out a durable policy name for z. You are probably trying to checkpoint a custom policy. 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To fix this, make sure you add your custom policy in rllib.algorithms.registry.POLICIES.r:) ray.rllib.algorithms.registryrRr;loggerwarningr&r<r=r>)r?rRrNs r@ serializezPolicySpec.serializesGGGGGG$#D$566 ; NNG@QGGG   #C !.t/E!F!F)$*;<<k    rBspeccVt|dtrddlm}||d}nr:) isinstancestrrSrYtypeAttributeErrorr%)rNrWrYr;s r@ deserializezPolicySpec.deserializes d>*C 0 0 V F F F F F F++D,@AALL ^,d 3 3 V/LL !Td>>R!T!TUU Us%-d3F.GHH(n)=>>>     rB)NNNN)rCr7)__name__ __module__ __qualname____doc__rArErr[rrI classmethodrPrVr^rBr@r7r7Ss  TX      4S>    tCH~,[ 4    0 t    [   rBr7ceZdZdZdejdejdefdZe dcde e e fde e ed e deedfffd Zed ed dfd Zd Zd efdZdddZ deddddddddde ed e eede ede edede ede ede ed eeeeee efffdZ dfde eee effde ede ed eeeeee efffdZe dgde eeefde eede eeefde eeefde ee e!fde ede ede ed eeeeee efffd Z" dhd"e eeefde eeefde eede e eeefde e eeefd#ed$ed efd%Z#e$ ded&ed'e ee%edeffd efd(Z&e'd)e(d*e)d+ed e eeeffd,Z*d-ed ee effd.Z+d/e,d0ed ee effd1Z-did3ed4ed efd5Z.did4ed efd6Z/djd7ed4efd8Z0d9ed ee1ee efffd:Z2d;e1d dfd<Z3d e4fd=Z5d>e4d dfd?Z6d ee effd@Z7d efdAZ8d efdBZ9d eefdCZ:e$d efdDZ;d efdEZde e?e e?ge@fd e@fdHZAdIee efd dfdJZBeCfddKdLdMe dNe edOe d dfdPZDdcdMe dQe ed dfdRZEdSe d dfdTZFd e dUfdVZGd e fdWZHd efdXZId eJfdYZKdZZL dkd[ed dfd\ZMdee effd]ZN dld_ed efd`ZOdaZPdbZQdS)mPolicyaGRLlib's base class for all Policy implementations. Policy is the abstract superclass for all DL-framework specific sub-classes (e.g. TFPolicy or TorchPolicy). It exposes APIs to 1. Compute actions from observation (and possibly other) inputs. 2. Manage the Policy's NN model(s), like exporting and loading their weights. 3. Postprocess a given trajectory from the environment or other input via the `postprocess_trajectory` method. 4. Compute losses from a train batch. 5. Perform updates from a train batch on the NN-models (this normally includes loss calculations) either: a. in one monolithic step (`learn_on_batch`) b. via batch pre-loading, then n steps of actual loss computations and updates (`load_batch_into_buffer` + `learn_on_loaded_batch`). r<r=r>c||_||_|d|_t ||_t ||_||_|jd|_ddl m }|jd}t||r||_ nqt|ttfrF t|jd|_ n#t $rYnwxYw||_ d|_d|_d|_|d|_d|_d|_dS)aCInitializes a Policy instance. Args: observation_space: Observation space of the policy. action_space: Action space of the policy. config: A complete Algorithm/Policy config dict. For the default config keys and values, see rllib/algorithm/algorithm.py. __policy_id frameworkr) RLlibCallback callbacksNF)r<r=get_Policy__policy_idr'observation_space_structaction_space_structr>riray.rllib.callbacks.callbacksrjrZrkr[r\r$ Exceptionglobal_timestepnum_grad_updates dist_classinit_view_requirements%_model_init_state_automatically_addedagent_connectorsaction_connectors)r?r<r=r>rjrks r@rAzPolicy.__init__s->'3!::m44)CCT(U(U%#=l#K#K +1 55 @?????KOOK00 i / / >&Y[[DNN  C; / / > 32BKOOK003333    /rOrzcheckpoint_dirpoliciesrfformatmsgpackTerror)rray.rllib.algorithms.algorithmr|rVersionopenpickleloadloadsitemsmerge_algorithm_configsrMrfrPospathjoinrlr)ryrzcheckpoint_infor|rfrJ worker_state policy_statespid policy_stateserialized_policy_specr policy_idpolicy_checkpoint_infors r@from_checkpointzPolicy.from_checkpointsw>.j99 6 "k 1 1 @ @ @ @ @ @H34wu7M7MMM/,7>>+!"KNNE+++++++++++++++ &|E(O<< ,W 5 )6)<)<)>)>DD%C-9.-I#-N*$-$E$E$_57Mh7W%%M+118]2KLLL ''8N(OPPP$*$5$5l$C$CHSMMD!.:!0!> N NI!)Y*-D-D1DGLL /0@ A * )22. 6f=IIII!"8"FMM:QR+1;q>>L:::::::::::::::.4.?.? .M.M+OG""8,, 99,4888ol3T:: +a&#LLOOEE"KNNE  + + + + + + + + + + + + + + + $$U++ +s6A::A>A>-GG G :-I33I7:I7rJct|d}|tdt|}t |j|j}|jddkrddlm}| |S||j |j |j}| ||S)a:Recovers a Policy from a state object. The `state` of an instantiated Policy can be retrieved by calling its `get_state` method. This only works for the V2 Policy classes (EagerTFPolicyV2, SynamicTFPolicyV2, and TorchPolicyV2). It contains all information necessary to create the Policy. No access to the original code (e.g. configs, knowledge of the policy's class, etc..) is needed. Args: state: The state to recover a new Policy instance from. Returns: A new Policy instance. rONzJNo `policy_spec` key was found in given `state`! Cannot create new Policy.ritfr)TFPolicy) rl ValueErrorr7r^r+r;r>ray.rllib.policy.tf_policyr_tf1_from_state_helperr<r= set_state)rJserialized_pol_specpol_spec actual_classr new_policys r@rPzPolicy.from_stategs /4ii .F.F  &, ))*=>>4  ! O  ?; '4 / / ; ; ; ; ; ;22599 9"\  &  ! O    U###rBc|}t|ds ||_dS|D]\}}||jvr ||j|<dS)zMaximal view requirements dict for `learn_on_batch()` and `compute_actions` calls. Specific policies can override this function to provide custom list of view requirements. view_requirementsN)_get_default_view_requirementshasattrrr)r? view_reqskvs r@ruzPolicy.init_view_requirementssy7799 t011 2%.D " " "!)) 2 21D22201D*1- 2 2rBcd|jjDd|jjDdS)z&Get metrics on timing from connectors.c0i|]\}}|dzd|jzS_msimean.0nametimers r@ z0Policy.get_connector_metrics..s9!!!D%u dUZ/!!!rBc0i|]\}}|dzd|jzSrrrs r@rz0Policy.get_connector_metrics..s9"""D%u dUZ/"""rB)rwrx)rwtimersrrHs r@get_connector_metricszPolicy.get_connector_metricssm!!#'#8#?#E#E#G#G!!!""#'#8#?#E#E#G#G"""   rBcr|j||j|dS)z3Reset action- and agent-connectors for this policy.)env_idN)rwresetrx)r?rs r@reset_connectorszPolicy.reset_connectorss< ##6#222 $$F$33333rB) prev_action prev_rewardinfo input_dictepisodeexploretimestepobsrrrrrrc B|`tj|i}|t|D] \} } | |d| <|||tj<|||tj<|||tj<t jd|}d} ||g} |t|| || }t|ts|}g}i}n|\}}}t|}t|dksJ|d}|t j d|t j d|fS) aComputes and returns a single (B=1) action value. Takes an input dict (usually a SampleBatch) as its main data input. This allows for using this method in case a more complex input pattern (view requirements) is needed, for example when the Model requires the last n observations, the last m actions/rewards, or a combination of any of these. Alternatively, in case no complex inputs are required, takes a single `obs` values (and possibly single state values, prev-action/reward values, etc..). Args: obs: Single observation. state: List of RNN state inputs, if any. prev_action: Previous action value, if any. prev_reward: Previous reward, if any. info: Info object, if any. input_dict: A SampleBatch or input dict containing the single (unbatched) Tensors to compute actions. If given, it'll be used instead of `obs`, `state`, `prev_action|reward`, and `info`. episode: This provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. explore: Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep: The current (sampling) time step. Keyword Args: kwargs: Forward compatibility placeholder. Returns: Tuple consisting of the action, the list of RNN state outputs (if any), and a dictionary of extra features (if any). N state_in_c|dkr|nJtr/t|tjr|dnt j|dS)Nseq_lensr)torchrZTensor unsqueezenp expand_dims)pss r@z.Policy.compute_single_action..sR ??*'5<88*Q[[^^^^Aq)) rB)repisodesrrrc|dSNrrdxs r@rz.Policy.compute_single_action..$ 1rBc|dSrrdrs r@rz.Policy.compute_single_action..%rrB)rOBS enumerate PREV_ACTIONS PREV_REWARDSINFOStreemap_structure_with_pathcompute_actions_from_input_dictrZtupler)len map_structure)r?rrJrrrrrrrkwargsirrout single_action state_outbatched_actions r@compute_single_actionzPolicy.compute_single_actionsh  %/3/J %e,,44DAq23J1//&7B ;34&7B ;3404 ;,-1        yH22":.. 3  #u%% 4MIDD/2 +NIt#N33M=!!Q&&&&%a(    ~~y 9 9  ~~t 4 4  rBc 8d|D}|j|tj|f|tj|tj|tj|||d|S)aComputes actions from collected samples (across multiple-agents). Takes an input dict (usually a SampleBatch) as its main data input. This allows for using this method in case a more complex input pattern (view requirements) is needed, for example when the Model requires the last n observations, the last m actions/rewards, or a combination of any of these. Args: input_dict: A SampleBatch or input dict containing the Tensors to compute actions. `input_dict` already abides to the Policy's as well as the Model's view requirements and can thus be passed to the Model as-is. explore: Whether to pick an exploitation or exploration action (default: None -> use self.config["explore"]). timestep: The current (sampling) time step. episodes: This provides access to all of the internal episodes' state, which may be useful for model-based or multi-agent algorithms. Keyword Args: kwargs: Forward compatibility placeholder. Returns: actions: Batch of output actions, with shape like [BATCH_SIZE, ACTION_SHAPE]. state_outs: List of RNN state output batches, if any, each with shape [BATCH_SIZE, STATE_SIZE]. info: Dictionary of extra feature batches, if any, with shape like {"f1": [BATCH_SIZE, ...], "f2": [BATCH_SIZE, ...]}. cBg|]\}}|d|S)state_in) startswith)rrrs r@ z:Policy.compute_actions_from_input_dict..Qs-VVVtq!Q\\*=U=UVVVVrB)prev_action_batchprev_reward_batch info_batchrrr)rcompute_actionsrrrlrrr)r?rrrrr state_batchess r@rz&Policy.compute_actions_from_input_dict(sRWVz'7'7'9'9VVV #t# { '   )nn[-EFF(nn[-EFF!~~k&788      rB obs_batchrrrrrc  t)aFComputes actions for the current policy. Args: obs_batch: Batch of observations. state_batches: List of RNN state input batches, if any. prev_action_batch: Batch of previous action values. prev_reward_batch: Batch of previous rewards. info_batch: Batch of info objects. episodes: List of Episode objects, one for each obs in obs_batch. This provides access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. explore: Whether to pick an exploitation or exploration action. Set to None (default) for using the value of `self.config["explore"]`. timestep: The current (sampling) time step. Keyword Args: kwargs: Forward compatibility placeholder Returns: actions: Batch of output actions, with shape like [BATCH_SIZE, ACTION_SHAPE]. state_outs (List[TensorType]): List of RNN state output batches, if any, each with shape [BATCH_SIZE, STATE_SIZE]. info (List[dict]): Dictionary of extra feature batches, if any, with shape like {"f1": [BATCH_SIZE, ...], "f2": [BATCH_SIZE, ...]}. NotImplementedError) r?rrrrrrrrrs r@rzPolicy.compute_actions^s T"!rBTactionsactions_normalized in_trainingct)aEComputes the log-prob/likelihood for a given action and observation. The log-likelihood is calculated using this Policy's action distribution class (self.dist_class). Args: actions: Batch of actions, for which to retrieve the log-probs/likelihoods (given all other inputs: obs, states, ..). obs_batch: Batch of observations. state_batches: List of RNN state input batches, if any. prev_action_batch: Batch of previous action values. prev_reward_batch: Batch of previous rewards. actions_normalized: Is the given `actions` already normalized (between -1.0 and 1.0) or not? If not and `normalize_actions=True`, we need to normalize the given actions first, before calculating log likelihoods. in_training: Whether to use the forward_train() or forward_exploration() of the underlying RLModule. Returns: Batch of log probs/likelihoods, with shape: [BATCH_SIZE]. r)r?rrrrrrrs r@compute_log_likelihoodszPolicy.compute_log_likelihoodss @"!rB sample_batchother_agent_batchesc|S)a$Implements algorithm-specific trajectory postprocessing. This will be called on each trajectory fragment computed during policy evaluation. Each fragment is guaranteed to be only from one episode. The given fragment may or may not contain the end of this episode, depending on the `batch_mode=truncate_episodes|complete_episodes`, `rollout_fragment_length`, and other settings. Args: sample_batch: batch of experiences for the policy, which will contain at most one episode trajectory. other_agent_batches: In a multi-agent env, this contains a mapping of agent ids to (policy, agent_batch) tuples containing the policy and experiences of the other agents. episode: An optional multi-agent episode object to provide access to all of the internal episode state, which may be useful for model-based or multi-agent algorithms. Returns: The postprocessed sample batch. rd)r?rrrs r@postprocess_trajectoryzPolicy.postprocess_trajectorys >rBmodelrt train_batchct)aLoss function for this Policy. Override this method in order to implement custom loss computations. Args: model: The model to calculate the loss(es). dist_class: The action distribution class to sample actions from the model's outputs. train_batch: The input batch on which to calculate the loss. Returns: Either a single loss tensor or a list of loss tensors. r)r?rrtrs r@lossz Policy.losss ""!rBsamplesc`||\}}|||S)aPerform one learning update, given `samples`. Either this method or the combination of `compute_gradients` and `apply_gradients` must be implemented by subclasses. Args: samples: The SampleBatch object to learn from. Returns: Dictionary of extra metadata from `compute_gradients()`. .. testcode:: :skipif: True policy, sample_batch = ... policy.learn_on_batch(sample_batch) )compute_gradientsapply_gradients)r?r grads grad_infos r@learn_on_batchzPolicy.learn_on_batchs6( 11'::y U###rB replay_actorrc:tj|j|}|iSt |drFt |jdkr.||d|ddS| |S)aSamples a batch from given replay actor and performs an update. Args: replay_actor: The replay buffer actor to sample from. policy_id: The ID of this policy. Returns: Dictionary of extra metadata from `compute_gradients()`. )rNdevicesrr) buffer_index)offsetr) rayrlreplayremoterrrload_batch_into_bufferlearn_on_loaded_batchr)r?rrbatchs r@!learn_on_batch_from_replay_bufferz(Policy.learn_on_batch_from_replay_buffers" +22Y2GGHH =I 4 # # .DL(9(9A(=(=  ' 'A ' > > >--QQ-GG G&&u-- -rBrrrct)a\Bulk-loads the given SampleBatch into the devices' memories. The data is split equally across all the Policy's devices. If the data is not evenly divisible by the batch size, excess data should be discarded. Args: batch: The SampleBatch to load. buffer_index: The index of the buffer (a MultiGPUTowerStack) to use on the devices. The number of buffers on each device depends on the value of the `num_multi_gpu_tower_stacks` config key. Returns: The number of tuples loaded per device. r)r?rrs r@rzPolicy.load_batch_into_buffer "!rBct)aReturns the number of currently loaded samples in the given buffer. Args: buffer_index: The index of the buffer (a MultiGPUTowerStack) to use on the devices. The number of buffers on each device depends on the value of the `num_multi_gpu_tower_stacks` config key. Returns: The number of tuples loaded per device. r)r?rs r@"get_num_samples_loaded_into_bufferz)Policy.get_num_samples_loaded_into_buffer' "!rBrct)aRuns a single step of SGD on an already loaded data in a buffer. Runs an SGD step over a slice of the pre-loaded batch, offset by the `offset` argument (useful for performing n minibatch SGD updates repeatedly on the same, already pre-loaded data). Updates the model weights based on the averaged per-device gradients. Args: offset: Offset into the preloaded data. Used for pre-loading a train-batch once to a device, then iterating over (subsampling through) this batch n times doing minibatch SGD. buffer_index: The index of the buffer (a MultiGPUTowerStack) to take the already pre-loaded data from. The number of buffers on each device depends on the value of the `num_multi_gpu_tower_stacks` config key. Returns: The outputs of extra_ops evaluated over the batch. r)r?rrs r@rzPolicy.learn_on_loaded_batch5s *"!rBpostprocessed_batchct)aComputes gradients given a batch of experiences. Either this in combination with `apply_gradients()` or `learn_on_batch()` must be implemented by subclasses. Args: postprocessed_batch: The SampleBatch object to use for calculating gradients. Returns: grads: List of gradient output values. grad_info: Extra policy-specific info values. r)r?r$s r@r zPolicy.compute_gradientsLrrB gradientsct)a%Applies the (previously) computed gradients. Either this in combination with `compute_gradients()` or `learn_on_batch()` must be implemented by subclasses. Args: gradients: The already calculated gradients to apply to this Policy. r)r?r&s r@rzPolicy.apply_gradients^ "!rBct)aReturns model weights. Note: The return value of this method will reside under the "weights" key in the return value of Policy.get_state(). Model weights are only one part of a Policy's state. Other state information contains: optimizer variables, exploration state, and global state vars such as the sampling timestep. Returns: Serializable copy or view of model weights. rrHs r@ get_weightszPolicy.get_weightsjr"rBweightsct)aOSets this Policy's model's weights. Note: Model weights are only one part of a Policy's state. Other state information contains: optimizer variables, exploration state, and global state vars such as the sampling timestep. Args: weights: Serializable copy or view of model weights. r)r?r+s r@ set_weightszPolicy.set_weightsxr(rBc4|jS)zReturns the state of this Policy's exploration component. Returns: Serializable information on the `self.exploration` object. ) explorationrIrHs r@get_exploration_statezPolicy.get_exploration_states ))+++rBcdS)zWhether this Policy holds a recurrent Model. Returns: True if this Policy has an RNN-based Model. FrdrHs r@ is_recurrentzPolicy.is_recurrents urBcdS)zThe number of internal states needed by the RNN-Model of the Policy. Returns: int: The number of RNN internal states kept by this Policy's Model. rrdrHs r@num_state_tensorszPolicy.num_state_tensorss qrBcgS)zReturns initial RNN state for the current policy. Returns: List[TensorType]: Initial RNN state for the current policy. rdrHs r@get_initial_statezPolicy.get_initial_states  rBcp||j|jd}tt ||j|j|j}||d<i}|j r|j |d<|j r|j |d<||d<|S)aReturns the entire current state of this Policy. Note: Not to be confused with an RNN model's internal state. State includes the Model(s)' weights, optimizer weights, the exploration component's state, as well as global variables, such as sampling timesteps. Note that the state may contain references to the original variables. This means that you may need to deepcopy() the state before mutating it. Returns: Serialized local state. )r+rrrsr:rOagentactionconnector_configs) r*rrrsr7r\r<r=r>rVrwto_staterx)r?rJrOr:s r@rIzPolicy.get_states"''))#3 $ 5   !d"4*;    +4466m   J)-)>)G)G)I)I g &  ! L*.*@*I*I*K*K h '%6!" rBcddlm}|di}d|vrd|||d|_tdt|jdd|vrf|||d|_td t|jdd Sd S) zRestore agent and action connectors if configs available. Args: state: The new state to set this policy to. Can be obtained by calling `self.get_state()`. r)restore_connectors_for_policyr:r8zrestoring agent connectors:) indentationr9zrestoring action connectors:N)ray.rllib.connectors.utilr=rlrwrTdebug__str__rx)r?rJr=r:s r@restore_connectorszPolicy.restore_connectorss LKKKKK!II&92>> ' ' '$A$A'0%%D ! LL6 7 7 7 LL.6616EE F F F ( ( (%B%B'1&&D " LL7 8 8 8 LL/77A7FF G G G G G ) (rBcd|vrt|d}|j;|j|jkr+td|jd|jd|j;|j|jkr+td|jd|jd|jr |j|_||d||dS) zRestores the entire current state of this Policy from `state`. Args: state: The new state to set this policy to. Can be obtained by calling `self.get_state()`. rONz+`observation_space` in given policy state (z2) does not match this Policy's observation space (z).z&`action_space` in given policy state (z-) does not match this Policy's action space (r+) r7r^r<rTrUr=r>r-rC)r?rJrOs r@rzPolicy.set_states1 E ! !$00}1EFFK-91T5KKKE"4EE*.*@EEE (4,0AAA;"/;;%)%6;;; ! 1)0  y)*** &&&&&rBfuncc||g|Ri|S)ahCalls the given function with this Policy instance. Useful for when the Policy class has been converted into a ActorHandle and the user needs to execute some functionality (e.g. add a property) on the underlying policy object. Args: func: The function to call, with this Policy as first argument, followed by args, and kwargs. args: Optional additional args to pass to the function call. kwargs: Optional additional kwargs to pass to the function call. Returns: The return value of the function call. rd)r?rEargsrs r@applyz Policy.apply s$*tD*4***6***rB global_varsc|jdkr!|j|dn |d|_|d}| ||_dSdS)zCalled on an update to global vars. Args: global_vars: Global variables by str key, broadcast from the driver. tf2rrsN)rirrassignrlrs)r?rIrss r@on_global_var_updatezPolicy.on_global_var_update$sl >U " "  ' ' J(? @ @ @ @#.z#:D &??+=>>  '$4D ! ! ! ( 'rB cloudpickle)rcheckpoint_format export_dirrrOc |tkrtdd|dvrtd|d||}t j|d|d krht |d <d }ttj ||d 5}tj ||dddn #1swxYwYnd dl m }td}tt |d <||dd|dd<d}ttj ||d 5}| ||dddn #1swxYwYttj |dd5}t#j dt|d ||t$jt$jd|dddn #1swxYwY|jdr5|tj |ddSdS)aExports Policy checkpoint to a local directory and returns an AIR Checkpoint. Args: export_dir: Local writable directory to store the AIR Checkpoint information into. policy_state: An optional PolicyState to write to disk. Used by `Algorithm.save_checkpoint()` to save on the additional `self.get_state()` calls of its different Policies. checkpoint_format: Either one of 'cloudpickle' or 'msgpack'. .. testcode:: :skipif: True from ray.rllib.algorithms.ppo import PPOTorchPolicy policy = PPOTorchPolicy(...) policy.export_checkpoint("/tmp/export_dir") z-Policy.export_checkpoint(filename_prefix=...)T)oldr)rNrzValue of `checkpoint_format` (z,) must either be 'cloudpickle' or 'msgpack'!N)exist_okrNr}zpolicy_state.pklzw+br)AlgorithmConfigrrOr>zpolicy_state.msgpckzrllib_checkpoint.jsonwrf)r\r}rr~ ray_version ray_commitexport_native_model_filesr)rrrrIrmakedirsrrrrrdump%ray.rllib.algorithms.algorithm_configrTrr[_serialize_dictjsonr __version__ __commit__r> export_model) r?rPfilename_prefixrrOr~rrTrs r@export_checkpointzPolicy.export_checkpoint8s)6 . . . C     $> > >.1B...   >>++L J....  - -1CL- .+Jbgll:z::EBB -a L!,,, - - - - - - - - - - - - - - - N M M M M M(t444G145G1H1HL- .4C4S4S]+H555L ' 1/Jbgll:z::EBB .a \1--- . . . . . . . . . . . . . . ."',,z+BCCS I I Q I$*-l;O.P*Q*Q/",#&?"%.                    ;2 3 A   bgll:w?? @ @ @ @ @ A As7 CC CE88E<?E<1AHHHonnxct)alExports the Policy's Model to local directory for serving. Note: The file format will depend on the deep learning framework used. See the child classed of Policy and their `export_model` implementations for more details. Args: export_dir: Local writable directory. onnx: If given, will export model in ONNX format. The value of this parameter set the ONNX OpSet version to use. Raises: ValueError: If a native DL-framework based model (e.g. a keras Model) cannot be saved to disk for various reasons. r)r?rPrcs r@r`zPolicy.export_modelrrB import_filect)zeImports Policy from local file. Args: import_file: Local readable file. r)r?res r@import_model_from_h5zPolicy.import_model_from_h5s "!rBz tf1.SessioncdS)a\Returns tf.Session object to use for computing actions or None. Note: This method only applies to TFPolicy sub-classes. All other sub-classes should expect a None to be returned from this method. Returns: The tf Session to use for computing actions and losses with this policy or None. NrdrHs r@ get_sessionzPolicy.get_sessions trBc(tjS)zReturns the computer's network name. Returns: The computer's networks name or an empty string, if the network name could not be determined. )platformnoderHs r@get_hostzPolicy.get_hosts}rBc|jdd}|jdd}tjjtjjjkr|sd}n!|dkr|jd}n |jd}|dkrd}nd||rd nd }t d |dkr|nd ||S) zDecide on the number of CPU/GPU nodes this policy should run on. Return: 0 if policy should run on CPU. >0 if policy should run on 1 or more GPUs. worker_indexr _fake_gpusFnum_gpusnum_gpus_per_env_runnerCPUz{} {}z fake-GPUsGPUsz!Policy (worker={}) running on {}.local) r>rlr_privater_mode LOCAL_MODErrTr)r? worker_idx fake_gpusrqdevs r@_get_num_gpus_for_policyzPolicy._get_num_gpus_for_policys[__^Q77 KOOL%88  L  % % ' '3<+>+I I I JHH 1__{:.HH{#<=H q==CC..)+O;;PPC / 6 6(1nn '3     rBct|dd|jStt|jdddi|j|jt|dd|jdd|jd dt|d |jd d  }|S) aRCreates the Policy's Exploration object. This method only exists b/c some Algorithms do not use TfPolicy nor TorchPolicy, but inherit directly from Policy. Others inherit from TfPolicy w/o using DynamicTFPolicy. Returns: Exploration: The Exploration object to be used by this Policy. r/Nexploration_configr\StochasticSamplingrnum_env_runnersrrorir)r=rr num_workersrori)getattrr/r"rr>rlr=)r?r/s r@_create_explorationzPolicy._create_explorations 4 - - 9# #!  KOO06;O2P Q Q*+$.. (91==;;dKd1S1STT    rBctjt|jtjttjd|jdtjt|jdtjttjd|jtjttj ttjdtj ttj ttj td tj ttjttjttjti S) aVReturns a default ViewRequirements dict. Note: This is the base/maximum requirement dict, from which later some requirements will be subtracted again automatically to streamline data collection, batch creation, and data transfer. Returns: ViewReqDict: The default view requirements dict. spacerF)data_colshiftrused_for_compute_actionsrr)rrr)rrr)rrrr<NEXT_OBSACTIONSr=rREWARDSr TERMINATEDS TRUNCATEDSrEPS_ID UNROLL_ID AGENT_INDEXr2rHs r@rz%Policy._get_default_view_requirementss+ O_43IJJJ  /$,). ###  '%"""  $o$,Bd>O'''  !2!2  $o$,B'''  #_%6%6  "O$5$5  NNN   1 1  !?#4#4  #_%6%6 M?,,9  rBauto_remove_unneeded_view_reqsc |jddrdSd|_tt |j}t t|jdzd|jd}| ||_ | |j d}| |j |\}}}|j D]\} } | |j jvrd| _|D]q\} } | |j | <| |j vrYt#| t$t&jfr)t+| } t-| d |j | <bt/d r|j jD]3} | |j vr(t-|j | <d|j | _4| |} |j d| D]}||j vr| ||j |<|j j|j j|j j|jr |j||j ||j }d}|r d}d }d ||vr|d |d||d |<d ||vr9|d |d||d |<|dz }d ||v||zt'jfdtA|Dt&j!}||tDj#<| |}|$d|||tDj#<|j j%|_%|j&#|&||j'|j(|nBtS|j*r.|jds!|*|j'|j(|| |||tW|dr"|jds|,|d|_|r|j|j jz|j jz}|D]3} | |j vr(| tDj#krt-d|j | <4|j&stS|j*r|j jD]} | |jvr| |j vr|| |j'j vrn| tDj-tDj.tDj/tDj0tDj1tDj2tDj3tDj4fvrd|j | _5tm|j 7D]} | |vr| tDj-tDj.tDj/tDj0tDj1tDj2tDj3tDj4fvr_| |j'j vrQ| |j jvr.tp9d| |jd|j | =tu|jtur ||_dSt#|jtvj<r|j=|dSt/d|tu|j)arPerforms test calls through policy's model and loss. NOTE: This base method should work for define-by-run Policies such as torch and tf-eager policies. If required, will thereby detect automatically, which data views are required by a) the forward pass, b) the postprocessing, and c) the loss functions, and remove those from self.view_requirements that are not necessary for these computations (to save data storage and transfer). Args: auto_remove_unneeded_view_reqs: Whether to automatically remove those ViewRequirements records from self.view_requirements that are not needed. stats_fn (Optional[Callable[[Policy, SampleBatch], Dict[str, TensorType]]]): An optional stats function to be called after the loss. )_disable_initialize_loss_from_dummy_batchFNTr> train_batch_size)rrzqpolicy.compute_actions_from_input_dict() returns an extra action output that is neither a numpy array nor a dict.r state_in_{} state_out_{}rcg|]}Srdrd)r_seq_lens r@rz.s ; ; ;Q ; ; ;rB)dtype in_evaluationstats_fnrzSampleBatch key '{}' was deleted manually in postprocessing function! RLlib will automatically remove non-used items from the data stream. Remove the `del` from your postprocessing function.outputzpVariable self.global_timestep of policy {} needs to be either of type `int` or `tf.Variable`, but is of type {}.)>r>rl _no_tracingintr#rrminmaxbatch_divisibility_req'_get_dummy_batch_from_view_requirements _dummy_batch_lazy_tensor_dictrrr accessed_keysrrZdictrndarray$get_gym_space_from_struct_of_tensorsrrset_get_interceptorclear deleted_keys added_keysr/rrarrayrangeint32rSEQ_LENS set_trainingcount_lossrrtrr rrrrrrrrrr2used_for_traininglistkeysrTrUr\rVariablerL)r?rrglobal_ts_before_initsample_batch_sizerr state_outs extra_outskeyview_reqvaluer new_batchrr$rBrrall_accessed_keysrs @r@!_initialize_loss_from_dummy_batchz(Policy._initialize_loss_from_dummy_batchs0 ;??F N N  F   #$4T5I$J$J K K +a/ 4 4 K* +  !HH    t0111*.*N*N  w+O+ + 'Z"399;; : :MC$+999491%**,,  JC%*D c "$000edBJ%788 @GGE2A#e333D*3//%X1$2 M MC$000.=.?.?&s+GL&s+D@@ARSS  --d333 4 4A)))'0|!!$ '--/// &,,... $**,,,   M   3 3D$:K L L L"99$:KLL  AAA&&q))-@@@?R!((++@1"@#M$8$8$;$;<"((++/BBBDW&--a00EqbE'(=(=a(@(@AQ &&q))-@@@(1,Gx ; ; ; ;%(( ; ; ;28LLLH8@  4 5,,-@AA   &&&  08K , - -3  : ! JJtTZ+ F F F F 49 % % @dk/.J @ IIdj$/; ? ? ?   HT; ' ' ' 4 $ $ 'T[-I ' MM+ & & &! *B <)#12#./  )  d444 @T9T9T2A16333D*3/z6 <]49556 < ,:NNC;#<<<4#999tz'CCC'.'3'1'3'2'/'-'M   IN.s3E  6 ; ; = =>>< > > > >%%+VD$t7K2L2L%M%M rBct)zRemoves a time dimension for recurrent RLModules. Args: input_dict: The input dict. Returns: The input dict with a possibly removed time dimension. r)r?rs r@maybe_remove_time_dimensionz"Policy.maybe_remove_time_dimensions "!rBr batch_sizec:i}|jD]o\}jp|}tjt jjt jjfr|tj kr |j dr*|tj krr|j dsXtjj|j d\}}t!j|f|ddzt j||<tjt jjrJt)jdkrt)jnd}t-j||||<Qfdt/|D||<qt|S) zCreates a numpy dummy batch based on the Policy's view requirements. Args: batch_size: The size of the batch to create. Returns: Dict[str, TensorType]: The dummy batch containing all zero values. _disable_preprocessor_api_disable_action_flatteningri)rirN)r time_sizecg|] }j Srdr)rrrs r@rzBPolicy._get_dummy_batch_from_view_requirements..*s$O$O$OX^$O$O$OrB)rrrrZrgymspacesr rrrr>rrlrget_action_shaperzerosfloat32Spacer shift_arrr(r) r?rretview_colrrshaperrs @r@rz.Policy._get_dummy_batch_from_view_requirementss"&"8">">"@"@ P P Hh(4HH8>CJ,??P36x7I3J3JQ3N3NH.///TX%> :%%%CMM%P$O$O$OU:=N=N$O$O$OCMM3rBc  d|_t|dd p|} r6t ds&tjt |ji _|j}g}t|dr)t|j r| }nItr.t tj j rd|vr fd|_ nd |_ d|vrd |_|gt|dr|jgngz}t|D]L\}}t|t jrt n)t$r!t$|rt$nd}|r3||d krt+d d |jn|}n|}|D]}d||vrjt d|dd|jdidd||d|<d||vr't |d|d|<NdS)a Uses Model's (or this Policy's) init state to add needed ViewReqs. Can be called from within a Policy to make sure RNNs automatically update their internal state-related view requirements. Changes the `self.view_requirements` dict. 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