ml-agents and A.I.-Jumping-Cars-ML-Agents-Example
ML-Agents is the foundational reinforcement learning framework that Jumping-Cars uses as a dependency to demonstrate practical agent training within Unity3D, making them complementary tools rather than alternatives.
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 A.I.-Jumping-Cars-ML-Agents-Example
Sebastian-Schuchmann/A.I.-Jumping-Cars-ML-Agents-Example
Ultimate Walkthrough Example for ML-Agents 1.0+ in Unity3D
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