on-policy
★ 2.0k
vs
virtualhome
★ 617
on-policy vs virtualhome
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.; virtualhome: VirtualHome is an interactive platform designed to simulate complex household activities using programs and a Python API. It supports rich environmental interactions, multi-agent activities, and serves as an environment for embodied AI and reinforcement learning agent training.
01
TL;DR
Choose on-policy if…Research and experimentation in cooperative multi-agent reinforcement learning
Choose virtualhome if…Training agents for embodied AI and Reinforcement Learning tasks.
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
virtualhome01Simulates complex household activities through programs via a Python API.
02Enables rich interactions with the environment, including picking up objects and operating appliances.
03Features procedural generation to create an infinite variety of unique environments.
04Supports multi-agent activities and allows dynamic modification of environments.
05Provides streaming of ground-truth data (segmentation, optical flow, depth) and OpenAI Gym-like RL environments.
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
virtualhome↳Training agents for embodied AI and Reinforcement Learning tasks.
↳Simulating and visualizing complex household activities through programs.
↳Generating large-scale datasets of programs and environment states for research.
05
Best For
on-policyTrendingReinforcement LearningMulti-Agent AI
virtualhomeTrending
FAQ
FAQ
What is the difference between on-policy and virtualhome?
Both on-policy and virtualhome are in the LLM Infra category. on-policy has 2.0k stars, while virtualhome has 617 stars.
Which is better, on-policy or virtualhome?
The best choice depends on your use case. Choose on-policy if Research and experimentation in cooperative multi-agent reinforcement learning, and virtualhome if Training agents for embodied AI and Reinforcement Learning tasks..
Is on-policy free or open source?
Yes, on-policy is open source on GitHub (MIT).
Is virtualhome free or open source?
Yes, virtualhome is open source on GitHub.
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