Next gymnasium

Who Should Read This Guide?¶

If you’re new to Gymnasium: You can probably skip this page! This guide is for users migrating from older versions of OpenAI Gym. If you’re just starting with RL, head to Basic Usage instead.

If you’re migrating from OpenAI Gym: This guide will help you update your code to work with Gymnasium. The changes are significant but straightforward once you understand the reasoning behind them.

If you’re updating old tutorials: Many online RL tutorials use the old v0.21 API. This guide shows you how to modernize that code.

Why Did the API Change?¶

Gymnasium is a fork of OpenAI Gym v0.26, which introduced breaking changes from Gym v0.21. These changes weren’t made lightly - they solved important problems that made RL research and development more difficult.

The main issues with the old API were:

• Ambiguous episode endings: The single flag couldn’t distinguish between “task completed” and “time limit reached”

• Inconsistent seeding: Random number generation was unreliable and hard to reproduce

• Rendering complexity: Switching between visual modes was unnecessarily complicated

• Reproducibility problems: Subtle bu

  The Gymnasium: Cultivating the Next Generation of World Farmer Leaders

  The Gymnasium is the WFO flagship capacity-development programme for young farmers, cultivating the next generation of resilient, innovative, and responsible global agricultural leaders. At its core, the programme integrates sustainability in all dimensions—aligned with the SDGs and corporate ESG principles—while adapting to current trends and challenges. The Gymnasium stands out by combining theory with practical experience, immersing participants in real advocacy events as WFO delegates, empowering them to engage directly with global policymakers and shape the future of agriculture.

  Beyond skill-building, the programme builds a strong global network of young farmers, fostering lasting connections, peer-to-peer learning, and collaboration across borders. The Gymnasium Alumni continue to exchange knowledge and experiences, strengthening their collective impact within the agricultural sector.

  Since its launch, the Gymnasium has trained young farmers from all regions of the world.

  A new Call for Expression of Interest for the next edition of the Gymnasium will be published soon. Stay tuned!

  Deep Dive: VectorEnv Autoreset

  In Gymnasium v1.0, significant changes were made to improve the implementation. One of these changes is how sub-environments are reset on termination (or truncation), referred to as the Autoreset Mode or API. Added in Gymnasium v1.1, Gymnasium’s built-in Vector environments and wrappers support for all autoreset modes / APIs, though the default with is next-step. In this blog post, we explain the differences in the possible modes and how to use them with example training code for each.

  Autoreset Modes

  Vector environments allow multiple sub-environments to run in parallel, improving training efficiency in Reinforcement Learning through sampling multiple episodes at the same time. What should the vector environment do when one or multiple sub-environments reset as it will need to be reset before future actions can be taken? There are three options referred to as Next-Step, Same-Step and Disabled mode, visualised in the figure below.

  Gymnasium’s built-in vector environment implementations, and support all three modes using the argument expecting a , for example, . Further, most of Gymnasium’s vector wrappers support all modes, however, for exter
  

  Taking Your Portfolio Case Studies to the Next Level

  Taking Your Portfolio Case Studies to the Next Level

  with Jeremy Osborn, Director of Learning at Aquent Gymnasium

  Jeremy Osborn: Case studies are one of the most powerful tools in your portfolio to help you demonstrate your expertise. Unfortunately, many of them don’t do a good job of keeping the reader engaged. In this tutorial, you’ll learn three ways to level up your case research and make you stand out from the crowd.

  Now a case study is a write-up of a project where you had a major role and one that you’re proud of.

  It gives the visitor insight into your thought process and your workflow. Here is a brief list of what you might detect in a good case study:

  • The client and deliverables
  • Your role and contributions
  • The process and approach used
  • The types of tools and techniques used
  • Key insights you learn from the project
  • What was the plan impact after it was released

  Tip number one: Use a narrative structure to engage the reader.

  To explain this tip, here’s an example of what not to do — add a couple of generic sentences describing a project followed by a dozen screenshots with no context. Please don’t do thi

  Env¶

  classgymnasium.Env[source]¶

  The main Gymnasium class for implementing Reinforcement Learning Agents environments.

  The class encapsulates an environment with arbitrary behind-the-scenes dynamics through the and functions. An environment can be partially or fully observed by single agents. For multi-agent environments, see PettingZoo.

  The main API methods that users of this class need to know are:

  • - Updates an environment with actions returning the next agent observation, the reward for taking that actions, if the environment has terminated or truncated due to the latest action and information from the environment about the step, i.e. metrics, debug info.

  • - Resets the environment to an initial state, required before calling step. Returns the first agent observation for an episode and information, i.e. metrics, debug info.

  • - Renders the environments to help visualise what the agent see, examples modes are “human”, “rgb_array”, “ansi” for text.

  • - Closes the environment, important when external software is used, i.e. pygame for rendering, databases

  Environments have additional attributes for users to understand the implementation

  • - The Space object