强化学习
计算机科学
马尔可夫决策过程
人工智能
循环神经网络
任务(项目管理)
机器学习
比例(比率)
国家(计算机科学)
马尔可夫过程
人工神经网络
算法
数学
统计
量子力学
物理
经济
管理
作者
Yan Duan,John Schulman,Xi Chen,Peter L. Bartlett,Ilya Sutskever,Pieter Abbeel
出处
期刊:Cornell University - arXiv
日期:2016-01-01
被引量:246
标识
DOI:10.48550/arxiv.1611.02779
摘要
Deep reinforcement learning (deep RL) has been successful in learning sophisticated behaviors automatically; however, the learning process requires a huge number of trials. In contrast, animals can learn new tasks in just a few trials, benefiting from their prior knowledge about the world. This paper seeks to bridge this gap. Rather than designing a "fast" reinforcement learning algorithm, we propose to represent it as a recurrent neural network (RNN) and learn it from data. In our proposed method, RL$^2$, the algorithm is encoded in the weights of the RNN, which are learned slowly through a general-purpose ("slow") RL algorithm. The RNN receives all information a typical RL algorithm would receive, including observations, actions, rewards, and termination flags; and it retains its state across episodes in a given Markov Decision Process (MDP). The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP. We evaluate RL$^2$ experimentally on both small-scale and large-scale problems. On the small-scale side, we train it to solve randomly generated multi-arm bandit problems and finite MDPs. After RL$^2$ is trained, its performance on new MDPs is close to human-designed algorithms with optimality guarantees. On the large-scale side, we test RL$^2$ on a vision-based navigation task and show that it scales up to high-dimensional problems.
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