机器之心·2025-10-02 01:30Core Insights - Google DeepMind's Dreamer 4 supports the idea that agents can learn skills for interacting with the physical world through imagination without direct interaction [2][4] - Dreamer 4 is the first agent to obtain diamonds in the challenging game Minecraft solely from standard offline datasets, demonstrating significant advancements in offline learning [7][21] Group 1: World Model and Training - World models enable agents to understand the world deeply and select successful actions by predicting future outcomes from their perspective [4] - Dreamer 4 utilizes a novel shortcut forcing objective and an efficient Transformer architecture to accurately learn complex object interactions while allowing real-time human interaction on a single GPU [11][19] - The model can be trained on large amounts of unlabeled video data, requiring only a small amount of action-paired video, opening possibilities for learning general world knowledge from diverse online videos [13] Group 2: Experimental Results - In the offline diamond challenge, Dreamer 4 significantly outperformed OpenAI's offline agent VPT15, achieving success with 100 times less data [22] - Dreamer 4's performance in acquiring key items and the time taken to obtain them surpassed behavior cloning methods, indicating that world model representations are superior for decision-making [24] - The agent demonstrated a high success rate in various tasks, achieving 14 out of 16 successful interactions in the Minecraft environment, showcasing its robust capabilities [29] Group 3: Action Generation - Dreamer 4 achieved a PSNR of 53% and SSIM of 75% with only 10 hours of action training, indicating that the world model absorbs most knowledge from unlabeled videos with minimal action data [32]