{"repo":"qiwang067/LS-Imagine","free":true,"listed":false,"github":"https://github.com/qiwang067/LS-Imagine","clone":"git clone https://github.com/qiwang067/LS-Imagine.git","description":"[ICLR 2025 Oral] PyTorch code for the paper \"Open-World Reinforcement Learning over Long Short-Term Imagination\"","language":"Python","stars":235,"topics":["reinforcement-learning","rl","visual-reinforcement-learning","visual-rl","world-model","minecraft","minedojo","dreamer","dreamerv2","dreamerv3"],"license":"Apache-2.0","category":"machine-learning","readme_excerpt":"[ICLR 2025 Oral] Open-World Reinforcement Learning over Long Short-Term Imagination Jiajian Li · Qi Wang · Yunbo Wang · Xin Jin · Yang Li · Wenjun Zeng · Xiaokang Yang Paper       arXiv       Website    ⚡ Quick Start 📥 Checkpoints Download 📝 Citation Training visual reinforcement learning agents in a high-dimensional open world presents significant challenges. While various model-based methods have improved sample efficiency by learning interactive world models, these agents tend to be \"short-sighted\", as they are typically trained on short snippets of imagined experiences. We argue that the primary challenge in open-world decision-making is improving the exploration efficiency across a vast state space, especially for tasks that demand consideration of long-horizon payoffs. In this paper, we present LS-Imgine, which extends the imagination horizon within a limited number of state transition steps, enabling the agent to explore behaviors that potentially lead to promising long-term feedback. The foundation of our approach is to build a long short-term world model . To achieve this, we simulate goal-conditioned jumpy state transitions and compute corresponding affordance maps by zooming in on specific areas within single images. This facilitates the integration of direct long-term values into behavior learning. Our method demonstrates significant improvements over state-of-the-art techniques in MineDojo. Showcases Harvest log","default_branch":null,"files":null,"tree":[],"storefront":"/r/qiwang067","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/qiwang067/LS-Imagine/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}