ml-agents and ml-agents-dodgeball-env
ML-Agents is the core framework for training agents in Unity environments, while ml-agents-dodgeball-env is a specific example environment that demonstrates and depends on the ML-Agents toolkit, making them complements designed to be used together.
About ml-agents
Unity-Technologies/ml-agents
The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Implements PyTorch-based algorithms (PPO, SAC, MA-POCA) for single and multi-agent scenarios, with support for imitation learning, curriculum learning, and environment randomization. Provides native cross-platform inference through its Inference Engine and exposes Unity environments as standard Python APIs (Gym, PettingZoo) for seamless integration with the broader ML ecosystem. Enables on-demand agent decision-making and concurrent training across multiple environment instances for scalable experiment iteration.
About ml-agents-dodgeball-env
Unity-Technologies/ml-agents-dodgeball-env
Showcase environment for ML-Agents
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