Skip to main content
Ctrl+K
Try Ray with $100 credit — Start now

Site Navigation

  • Get Started

  • Use Cases

  • Example Gallery

  • Library

    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

  • APIs

  • Resources

    • Discussion ForumGet your Ray questions answered

    • TrainingHands-on learning

    • BlogUpdates, best practices, user-stories

    • EventsWebinars, meetups, office hours

    • Success StoriesReal-world workload examples

    • EcosystemLibraries integrated with Ray

    • CommunityConnect with us

Try Managed Ray

Site Navigation

  • Get Started

  • Use Cases

  • Example Gallery

  • Library

    • Ray CoreScale general Python applications

    • Ray DataScale data ingest and preprocessing

    • Ray TrainScale machine learning training

    • Ray TuneScale hyperparameter tuning

    • Ray ServeScale model serving

    • Ray RLlibScale reinforcement learning

  • APIs

  • Resources

    • Discussion ForumGet your Ray questions answered

    • TrainingHands-on learning

    • BlogUpdates, best practices, user-stories

    • EventsWebinars, meetups, office hours

    • Success StoriesReal-world workload examples

    • EcosystemLibraries integrated with Ray

    • CommunityConnect with us

Try Managed Ray

Loading API navigation…

  • Ray APIs
  • Ray RLlib API
  • Environments
  • MultiAgentEpisode API
  • MultiAgentEpisode
  • env_t_to_agent_t

env_t_to_agent_t#

MultiAgentEpisode.env_t_to_agent_t: DefaultDict[Hashable | str, InfiniteLookbackBuffer]#

previous

agent_t_started

next

is_terminated
On this page
  • MultiAgentEpisode.env_t_to_agent_t
Thanks for the feedback!
Was this helpful?
Yes
No
Feedback
Submit

© Copyright 2026, The Ray Team.

Created using Sphinx 8.2.3.

Built with the PyData Sphinx Theme 0.18.0.