&`i fddlmZmZmZmZddlZddlZddl m Z ddl m Z ddl mZddlmZer ddlmZddlmZe dd ejd ed d eeefdedejf dZed ejdeeefdeddejfdZed ejdejfdZdS)) TYPE_CHECKINGAnyDictTypeN)Policy) SampleBatch) DeveloperAPI)convert_to_numpy) FQETorchModel)OffPolicyEstimatorTbatch model_classr model_statecompute_q_valuesreturnc ||}ttjtj|tjtjtj|tjdi}||}t|}||d<|r)| |}t|}||d<|S)aComputes the Q and V values for the given batch of samples. This function is to be used with map_batches() to perform a batch prediction on a dataset of records with `obs` and `actions` columns. Args: batch: A sub-batch from the dataset. model_class: The model class to use for the prediction. This class should be a sub-class of FQEModel that implements the estimate_q() and estimate_v() methods. model_state: The state of the model to use for the prediction. compute_q_values: Whether to compute the Q values or not. If False, only the V is computed and returned. Returns: The modified batch with the Q and V values added as columns. v_valuesq_values) from_staterOBSnpvstackACTIONSsqueeze estimate_vr estimate_q)r rrrmodel sample_batchrrs ~/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/offline/offline_evaluation_utils.pycompute_q_and_v_valuesr!s0  " "; / /E ORYu[_'=>>  51D+E!F!F!N!Nr!R!R L --H))H E*%##L11#H--$j L policy_stateestimator_classr c tj|}||dd}ttjt j|djtjt j|djdtj t j|djdtj t j|djdi}| |}|tj }|tj }||z } | |z} | |d<| |d <||d <||d <|S) a%Computes the importance sampling weights for the given batch of samples. For a lot of off-policy estimators, the importance sampling weights are computed as the propensity score ratio between the new and old policies (i.e. new_pi(act|obs) / old_pi(act|obs)). This function is to be used with map_batches() to perform a batch prediction on a dataset of records with `obs`, `actions`, `action_prob` and `rewards` columns. Args: batch: A sub-batch from the dataset. policy_state: The state of the policy to use for the prediction. estimator_class: The estimator class to use for the prediction. This class Returns: The modified batch with the importance sampling weights, weighted rewards, new and old propensities added as columns. r)policygammaepsilon_greedyobsactionsr action_probrewardsweightsweighted_rewardsnew_probold_prob) rrrrrrvaluesrr ACTION_PROBREWARDScompute_action_probs) r r#r$r& estimatorrr/r0r,r-r.s r compute_is_weightsr6=sA. | , ,FvQqIIII ORYuU|':;;  5+;+B!C!C!K!KB!O!O  #RYu]/C/J%K%K%S%STV%W%W  5+;+B!C!C!K!KB!O!O  L--l;;HK34H;./G!G(E) 0E  E* E* Lr"ctjtjtjtjtjtjh}|jD]%}||vr||d||<&|S)aRemoves the time dimension from the given sub-batch of the dataset. If each row in a dataset has a time dimension ([T, D]), and T=1, this function will remove the T dimension to convert each row to of shape [D]. If T > 1, the row is left unchanged. This function is to be used with map_batches(). Args: batch: The batch to remove the time dimension from. Returns: The modified batch with the time dimension removed (when applicable) c<t|dkr|dn|S)Nr)len)xs r z!remove_time_dim..sA! !r") rrrr2r3NEXT_OBSDONEScolumnsapply)r BATCHED_KEYSks r remove_time_dimrClsq  L]LL  Qx~~&J&JKKE!H Lr")T)typingrrrrnumpyrpandaspdray.rllib.policyrray.rllib.policy.sample_batchrray.rllib.utils.annotationsr ray.rllib.utils.numpyr ,ray.rllib.offline.estimators.fqe_torch_modelr 1ray.rllib.offline.estimators.off_policy_estimatorr DataFramestrboolr!r6rCr"r rRs111111111111######555555444444222222UJJJJJJTTTTTT " )) <)o&)c3h) ) \ ))))X+ <+sCx.+./+\ ++++\2<BLr"