Summary of Learning World Models For Unconstrained Goal Navigation, by Yuanlin Duan et al.
Learning World Models for Unconstrained Goal Navigation
by Yuanlin Duan, Wensen Mao, He Zhu
First submitted to arxiv on: 3 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm, MUN (World Models for Unconstrained Goal Navigation), enhances goal-conditioned reinforcement learning with sparse rewards by allowing agents to plan actions or exploratory goals without direct interaction with the environment. This approach promotes exploration efficiency and enables generalization of learned world models across state transitions between recorded trajectories or between different trajectories. The quality of a world model depends on the richness of data stored in the agent’s replay buffer, which is used for reasonable generalization within the state space surrounding recorded trajectories. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The algorithm MUN helps to learn policies that navigate between any “key” states by modeling state transitions between arbitrary subgoal states in the replay buffer. This approach significantly improves the policy’s capacity to generalize across new goal settings and strengthens the reliability of world models. By using a novel goal-directed exploration algorithm, this paper addresses challenges in generalizing learned world models to real-world dynamics. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
