Player-1 Framework Released: Revolutionizing Game AI Control via Synthetic Experience
Summary:Player-1 is a novel game AI control framework that leverages synthetic experience to surpass traditional supervised learning approaches. It extends the computer-use action space with relative pointer motion, sustained inputs, and composite actions, and is initialized via supervised fine-tuning on 6,044 curated gameplay trajectories. The Player-1-Gym, consisting of 16 synthetic 2D and 3D environments, supports multi-environment reinforcement learning with PPO. Experimental results show that Playe
Core Breakthrough
The core innovation of the Player-1 framework lies in its novel approach to game AI control through synthetic experience. The framework extends the computer-use action space with relative pointer motion, sustained inputs, and composite actions, closely mimicking human gaming interactions. It is initialized via supervised fine-tuning on 6,044 curated gameplay trajectories that pair recorded actions with task instructions and visual reasoning.
Technical Mechanism Analysis
The Player-1-Gym consists of 16 synthetic 2D and 3D environments designed around common gaming control demands. These environments employ programmatically verifiable rewards and support randomized observations and control dynamics to facilitate multi-environment reinforcement learning. Player-1 utilizes the PPO algorithm for training and incorporates composite actions to enhance training efficiency and performance.
Engineering Trade-offs and Performance
Player-1-9B and Player-1-35B-A3B improve MineDojo-300 success rates by 27.8 and 17.7 percentage points, respectively, demonstrating the significant potential of synthetic experience in enhancing game AI control. Player-1-9B also achieves a 38.33% success rate in ViZDoom-10 and GameWorld tasks, outperforming the strongest baseline model with a 12.63% success rate. Furthermore, experiments show that broader synthetic environment coverage improves transferability, while the use of composite actions increases during reinforcement learning.
Developer Deployment and Recommendations
For game AI developers, the Player-1 framework offers a new paradigm for overcoming the limitations of traditional supervised learning through synthetic experience. Developers can leverage the Player-1-Gym environments for training and combine the PPO algorithm with composite actions to optimize model performance. Additionally, the modular design of the framework makes it easy to extend and integrate into existing game AI systems.
— END —Source: Hugging Face Trending Papers (2026-10-14)
Tags: #Player-1 #Game AI #Reinforcement Learning #Synthetic Experience #Multi-Environment Training
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