Abstract
Learning physical dynamics in a series of non-stationary environments is a challenging but essential task for model-based reinforcement learning (MBRL) with visual inputs. It requires the agent to consistently adapt to novel tasks without forgetting previous knowledge. In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting. The key assumption is that an ideal world model can provide a non-forgetting environment simulator, which enables the agent to optimize the policy in a multi-task learning manner based on the imagined trajectories from the world model. To this end, we first propose the mixture world model that learns task-specific dynamics priors with a mixture of Gaussians, and then introduce a new training strategy to overcome catastrophic forgetting, which we call predictive experience replay. Finally, we extend these methods to continual RL and further address the value estimation problems with the exploratory-conservative behavior learning approach. Our model remarkably outperforms the naive combinations of existing continual learning and visual RL algorithms on DeepMind Control and Meta-World benchmarks with continual visual control tasks. It is also shown to effectively alleviate the forgetting of spatiotemporal dynamics in video prediction datasets with evolving domains.
Abstract (translated)
在学习一系列非稳定环境的物理动态特性是一项具有挑战性但是必不可少的任务,这对于使用视觉输入的模型驱动强化学习(MBRL)来说尤其如此。该任务要求代理持续适应新任务而不会忘记先前的知识。在本文中,我们提出了一种新的视觉动态建模方法,并探索了它在视觉控制和预测方面的效力。关键假设是理想的世界模型可以提供一种不会遗忘的环境模拟器,从而使代理能够在世界模型的想象轨迹上优化政策,以多任务学习的方式。为此,我们提出了一种混合世界模型,通过学习任务特定的动态先验分布,使用高斯混合模型来学习,然后引入了一种新的训练策略,以克服灾难性的遗忘,我们称之为预测经验回放。最后,我们将这些方法扩展到持续强化学习,并进一步解决了探索性保守行为学习方法所带来的价值估计问题。我们的模型在DeepMind控制和Meta-World基准任务中与现有的视觉强化学习和视觉控制算法的盲目组合相比表现出卓越的性能。它还表明,能够在具有进化域的视频预测数据集上有效地减轻忘记时序动态的问题。
URL
https://arxiv.org/abs/2303.06572