Learning in a lifelong setting, where the dynamics continually evolve, is a hard challenge for current reinforcement learning algorithms. Yet this would be a much needed feature for practical applications. In this paper, we propose an approach which learns a hyper-policy, whose input is time, that outputs the parameters of the policy to be queried at that time. This hyper-policy is trained to maximize the estimated future performance, efficiently reusing past data by means of importance sampling, at the cost of introducing a controlled bias. We combine the future performance estimate with the past performance to mitigate catastrophic forgetting. To avoid overfitting the collected data, we derive a differentiable variance bound that we embed as a penalization term. Finally, we empirically validate our approach, in comparison with state-of-the-art algorithms, on realistic environments, including water resource management and trading.

翻译：在生命期环境中学习,动态在不断演变,这是当前强化学习算法的艰巨挑战。然而,这将是当前强化学习算法的一个非常需要的特点。在本文中,我们提出一种方法,学习一种超政策,该政策的投入是时间的,即输出当时要问的政策参数。这一超政策经过培训,以最大限度地提高未来估计的绩效,以重要抽样方式有效地重复使用过去的数据,代价是引入一种受控的偏差。我们把未来业绩估计与过去的业绩评估结合起来,以缓解灾难性的遗忘。为避免过度适应所收集的数据,我们得出了一种不同的差异,作为惩罚性术语。最后,我们用经验验证了我们与最新算法相比,在现实环境中,包括水资源管理和贸易方面采用的方法。