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21 de ago. de 2023 · You can install these dependencies for one family like pip install "gymnasium[atari]" or use pip install "gymnasium[all]" to install all dependencies. We support and test for Python 3.8, 3.9, 3.10, 3.11 on Linux and macOS.
Gymnasium is a maintained fork of OpenAI’s Gym library. The Gymnasium interface is simple, pythonic, and capable of representing general RL problems, and has a compatibility wrapper for old Gym environments: import gymnasium as gym env = gym.make("LunarLander-v2", render_mode="human") observation, info = env.reset(seed=42) for _ in range(1000
You can install these dependencies for one family like pip install "gymnasium[atari]" or use pip install "gymnasium[all]" to install all dependencies. We support and test for Python 3.8, 3.9, 3.10, 3.11 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
We recommend that you use a virtual environment: git clone https://github.com/Farama-Foundation/gym-examples cd gym-examples python -m venv .env source .env/bin/activate pip install -e . Subclassing gymnasium.Env # Before learning how to create your own environment you should check out the documentation of Gymnasium’s API.
You can install these dependencies for one family like pip install gym[atari] or use pip install gym[all] to install all dependencies. We support Python 3.7, 3.8, 3.9 and 3.10 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
The Gym interface is simple, pythonic, and capable of representing general RL problems: All development of Gym has been moved to Gymnasium, a new package in the Farama Foundation that's maintained by the same team of developers who have maintained Gym for the past 18 months. If you're already using the latest release of Gym (v0.26.2), then you ...
The API contains four key functions: make, reset, step and render, that this basic usage will introduce you to. At the core of Gymnasium is Env, a high-level python class representing a markov decision process (MDP) from reinforcement learning theory (this is not a perfect reconstruction, and is missing several components of MDPs).
