on-policy
★ 2.0k
vs
gym-pybullet-drones
★ 2.0k
on-policy vs gym-pybullet-drones
on-policy: This repository implements MAPPO, a multi-agent variant of PPO, widely used in cooperative multi-agent games and research. It provides robust implementations for various multi-agent environments like StarCraft II, Hanabi, and Google Research Football, along with detailed training scripts and hyperparameter guidance.; gym-pybullet-drones: gym-pybullet-drones is a minimalist refactoring of its original repository, providing a Gym environment for simulating multi-agent quadcopter control. It is designed for compatibility with Gymnasium, Stable Baselines3 2.0, and various flight firmwares for hardware-in-the-loop simulation.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose gym-pybullet-drones if…Developing and evaluating PID controllers for quadcopter flight
02
Side-by-Side Comparison
03
Features
on-policy01Implementation of MAPPO (Multi-Agent PPO)
02Support for diverse multi-agent environments (e.g., StarCraft II, Hanabi)
03Ready-to-use training scripts for various scenarios
04Detailed hyperparameter guidance and updated results
05Default support for shared policy among agents
gym-pybullet-drones01Gymnasium and Stable Baselines3 2.0 compatibility
02Betaflight/Crazyflie firmware SITL integration
03PID control examples for drone navigation
04Reinforcement learning (PPO) for single and multi-agent quadcopter control
05Multiplatform support for Ubuntu and macOS
04
Use Cases
on-policy↳Research and experimentation in cooperative multi-agent reinforcement learning
↳Benchmarking and evaluating PPO's effectiveness in MARL scenarios
↳Training AI agents for popular multi-agent games like StarCraft II and Hanabi
gym-pybullet-drones↳Developing and evaluating PID controllers for quadcopter flight
↳Training single and multi-agent reinforcement learning policies for drone control tasks
↳Integrating with real flight firmwares like Betaflight for Software-in-the-Loop (SITL) simulations
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
gym-pybullet-dronesTrending
FAQ
FAQ
What is the difference between on-policy and gym-pybullet-drones?
Both on-policy and gym-pybullet-drones are in the LLM Infra category. on-policy has 2.0k stars, while gym-pybullet-drones has 2.0k stars.
Which is better, on-policy or gym-pybullet-drones?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and gym-pybullet-drones if Developing and evaluating PID controllers for quadcopter flight.
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is gym-pybullet-drones free or open source?
Yes, gym-pybullet-drones is open source on GitHub.
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