slimevolleygym
Basic Information
This repository provides a simple OpenAI Gym environment intended for use in single-agent and multi-agent reinforcement learning research and experimentation. It supplies a ready-made simulation that developers, researchers, and students can use to train, test, and compare RL algorithms within the Gym ecosystem. The environment is intended as a lightweight, focused testbed rather than a full application, so users can plug it into existing RL training pipelines to prototype behaviors, evaluate learning strategies, and explore interactions between agents. The repo targets people who need a convenient, Gym-compatible environment to study both isolated agent learning and competitive or cooperative multi-agent scenarios.
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App Details
Features
Gym API compatibility so agents and existing RL tooling can interact with the environment in a standard way. Support for both single-agent and multi-agent setups, enabling experiments that compare solitary learning with multi-agent dynamics. A simple, lightweight design aimed at ease of integration and rapid prototyping. Intended to be usable for education, research, and benchmarking of RL algorithms. The repository structure and naming indicate a focused simulation environment without requiring heavy infrastructure, making it straightforward to add to common training workflows.
Use Cases
The project helps practitioners accelerate development and evaluation of reinforcement learning approaches by providing a turnkey Gym environment for both single and multi-agent studies. It reduces setup time so researchers can focus on algorithm design, training regimes, and comparative experiments. Educators can use it as a classroom or lab exercise to demonstrate RL concepts and agent interactions. The standard Gym interface promotes reproducibility and interoperability with popular RL libraries and scripts, so results and benchmarks are easier to share and replicate across different projects and teams.
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