&`i ddlmZmZddlZddlmZddlmZedeefdZ edee deej j d eej j fd Z edeed eed dfd ZdS) )ListTupleN)SingleAgentEpisode) DeveloperAPIepisodesc0g}|D]}||j|xjdz c_|j|jd|j|jd|j|jd|jd|jD]}||d|j sd|_| |S)aAdds an artificial timestep to an episode at the end. In detail: The last observations, infos, actions, and all `extra_model_outputs` will be duplicated and appended to each episode's data. An extra 0.0 reward will be appended to the episode's rewards. The episode's timestep will be increased by 1. Also, adds the truncated=True flag to each episode if the episode is not already done (terminated or truncated). Useful for value function bootstrapping, where it is required to compute a forward pass for the very last timestep within the episode, i.e. using the following input dict: { obs=[final obs], state=[final state output], prev. reward=[final reward], etc.. } Args: episodes: The list of SingleAgentEpisode objects to extend by one timestep and add a truncation flag if necessary. Returns: A list of the original episodes' truncated values (so the episodes can be properly restored later into their original states). gT) append is_truncatedt observationsinfosactionsrewardsextra_model_outputsvaluesis_donevalidate)rorig_truncatedsepisodevs {/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/rllib/utils/postprocessing/episodes.py#add_one_ts_to_episodes_and_truncater s6Ow3444  Q  ##G$8$<=== W]2.///wr2333s###,3355  A HHQrUOOOO (#'G   episode_lensdatareturnc:g}d}|D]0}|t|||zdz ||z }1g}|D]5|tjfd|D6t |dkrt |n|dS)aRemoves the last timesteps from each given data item. Each item in data is a concatenated sequence of episodes data. For example if `episode_lens` is [2, 4], then data is a shape=(6,) ndarray. The returned corresponding value will have shape (4,), meaning both episodes have been shortened by exactly one timestep to 1 and 3. ..testcode:: from ray.rllib.algorithms.ppo.ppo_learner import PPOLearner import numpy as np unpadded = PPOLearner._remove_last_ts_from_data( [5, 3], np.array([0, 1, 2, 3, 4, 0, 1, 2]), ) assert (unpadded[0] == [0, 1, 2, 3, 0, 1]).all() unpadded = PPOLearner._remove_last_ts_from_data( [4, 2, 3], np.array([0, 1, 2, 3, 0, 1, 0, 1, 2]), np.array([4, 5, 6, 7, 2, 3, 3, 4, 5]), ) assert (unpadded[0] == [0, 1, 2, 0, 0, 1]).all() assert (unpadded[1] == [4, 5, 6, 2, 3, 4]).all() Args: episode_lens: A list of current episode lengths. The returned data will have the same lengths minus 1 timestep. data: A tuple of data items (np.ndarrays) representing concatenated episodes to be shortened by one timestep per episode. Note that only arrays with `shape=(n,)` are supported! The returned data will have `shape=(n-len(episode_lens),)` (each episode gets shortened by one timestep). Returns: A tuple of new data items shortened by one timestep. rr c g|] }| Sr!).0sds r z,remove_last_ts_from_data..rs"8"8"8A1Q4"8"8"8r)r slicenp concatenatelentuple)rrslicessumlen_retr$s @rremove_last_ts_from_datar/<sZF C eCta00111 t  C ;; 2>"8"8"8"8"8"8"899::::SA5:::3q61rrct||D]\}}|xjdzc_|j|j|j|j|jD]}|||_ dS)aIReverts the effects of `_add_ts_to_episodes_and_truncate`. Args: episodes: The list of SingleAgentEpisode objects to extend by one timestep and add a truncation flag if necessary. orig_truncateds: A list of the original episodes' truncated values to be applied to the `episodes`. r N) zipr rpoprrrrrr )rrrorig_truncatedrs r3remove_last_ts_from_episodes_and_restore_truncatedsr4vs$'x#A#A . . Q   """ ,3355  A EEGGGG- . .r)typingrrnumpyr'"ray.rllib.env.single_agent_episoderray.util.annotationsrrint_typingNDArrayr/boolr4r!rrr=sAAAAAA------/$7I2J////d62s)62 # $62 2: 62626262r.%&.$Z. ......r