o cin@s`ddlZddlZddlmZddlmZmZddlm Z m Z ddl m Z eGddde Z dS)N)Optional)override OldAPIStack) ExternalEnv_ExternalEnvEpisode)MultiAgentDictc @seZdZdZdejdejfddZddZee  dd e e d e d e fd dZ ee d e ded efddZee d e deded dfddZee   dd e dededed df ddZee d e ded dfddZdS)ExternalMultiAgentEnvz/This is the multi-agent version of ExternalEnv. action_spaceobservation_spacecCs^t|||t|jtst|jtr-|j|jks+td|j|jdSdS)zInitializes an ExternalMultiAgentEnv instance. Args: action_space: Action space of the env. observation_space: Observation space of the env. z=Agent ids disagree for action space and obs space dict: {} {}N) r__init__ isinstancer dictr keys ValueErrorformat)selfr r rZ/home/ubuntu/.local/lib/python3.10/site-packages/ray/rllib/env/external_multi_agent_env.pyr s zExternalMultiAgentEnv.__init__cCst)a Override this to implement the multi-agent run loop. Your loop should continuously: 1. Call self.start_episode(episode_id) 2. Call self.get_action(episode_id, obs_dict) -or- self.log_action(episode_id, obs_dict, action_dict) 3. Call self.log_returns(episode_id, reward_dict) 4. Call self.end_episode(episode_id, obs_dict) 5. Wait if nothing to do. Multiple episodes may be started at the same time. )NotImplementedError)rrrrrun'szExternalMultiAgentEnv.runNT episode_idtraining_enabledreturncCs^|dur tj}||jvrtd|||jvr!td|t||j|dd|j|<|S)Nz!Episode {} has already completed.zEpisode {} is already startedT) multiagent) uuiduuid4hex _finishedrr _episodesr_results_avail_condition)rrrrrr start_episode7s     z#ExternalMultiAgentEnv.start_episodeobservation_dictcCs||}||S)aRecord an observation and get the on-policy action. Thereby, observation_dict is expected to contain the observation of all agents acting in this episode step. Args: episode_id: Episode id returned from start_episode(). observation_dict: Current environment observation. Returns: action: Action from the env action space. )_getwait_for_actionrrr!episoderrr get_actionJs  z ExternalMultiAgentEnv.get_action action_dictcCs||}|||dS)aRecord an observation and (off-policy) action taken. Args: episode_id: Episode id returned from start_episode(). observation_dict: Current environment observation. action_dict: Action for the observation. N)r" log_action)rrr!r'r%rrrr(^s z ExternalMultiAgentEnv.log_action reward_dict info_dictmultiagent_done_dictc Csz||}|D]\}}||jvr|j||7<q ||j|<q |r2|D] \}}||j|<q(|r;|p7i|_dSdS)aRecord returns from the environment. The reward will be attributed to the previous action taken by the episode. Rewards accumulate until the next action. If no reward is logged before the next action, a reward of 0.0 is assumed. Args: episode_id: Episode id returned from start_episode(). reward_dict: Reward from the environment agents. info_dict: Optional info dict. multiagent_done_dict: Optional done dict for agents. N)r"itemscur_reward_dict cur_done_dict cur_info_dict) rrr)r*r+r%agentrewdonerrr log_returnsps    z!ExternalMultiAgentEnv.log_returnscCs&||}|j|j||dS)zRecord the end of an episode. Args: episode_id: Episode id returned from start_episode(). observation_dict: Current environment observation. N)r"raddrr2r$rrr end_episodes z!ExternalMultiAgentEnv.end_episode)NT)NN)__name__ __module__ __qualname____doc__gymSpacer rrrrstrboolr rr&r(r3r5rrrrr sf     %r)r gymnasiumr:typingrray.rllib.utils.annotationsrrray.rllib.env.external_envrrray.rllib.utils.typingrrrrrrs