PWM: Policy Learning with Multi-Task World Models
Keywords: reinforcement learning, model-based reinforcement learning, continuous control, world models
TL;DR: We propose an MBRL method that leverages pre-trained, multi-task world models for efficient policy learning using first-order optimization.
Abstract: Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World models methods offer scalability by learning a simulation of the environment, but often rely on inefficient gradient-free optimization methods for policy extraction. In contrast, gradient-based methods exhibit lower variance but fail to handle discontinuities. Our work reveals that well-regularized world models can generate smoother optimization landscapes than the actual dynamics, facilitating more effective first-order optimization. We introduce Policy learning with multi-task World Models (PWM), a novel model-based RL algorithm for continuous control. Initially, the world model is pre-trained on offline data, and then policies are extracted from it using first-order optimization in less than 10 minutes per task. PWM effectively solves tasks with up to 152 action dimensions and outperforms methods that use ground-truth dynamics. Additionally, PWM scales to an 80-task setting, achieving up to 27% higher rewards than existing baselines, without relying on costly online planning. Visualizations and code available at [https://policy-world-model.github.io/](https://policy-world-model.github.io/).
Primary Area: reinforcement learning
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Submission Number: 8505
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