Original Paper Information:
Policy Gradient and Actor-Critic Learning in Continuous Time and Space: Theory and Algorithms
Published 44522.
Category: Machine Learning
Authors:
[‘Yanwei Jia’, ‘Xun Yu Zhou’]
Original Abstract:
We study policy gradient (PG) for reinforcement learning in continuous timeand space under the regularized exploratory formulation developed by Wang etal. (2020). We represent the gradient of the value function with respect to agiven parameterized stochastic policy as the expected integration of anauxiliary running reward function that can be evaluated using samples and thecurrent value function. This effectively turns PG into a policy evaluation (PE)problem, enabling us to apply the martingale approach recently developed by Jiaand Zhou (2021) for PE to solve our PG problem. Based on this analysis, wepropose two types of the actor-critic algorithms for RL, where we learn andupdate value functions and policies simultaneously and alternatingly. The firsttype is based directly on the aforementioned representation which involvesfuture trajectories and hence is offline. The second type, designed for onlinelearning, employs the first-order condition of the policy gradient and turns itinto martingale orthogonality conditions. These conditions are thenincorporated using stochastic approximation when updating policies. Finally, wedemonstrate the algorithms by simulations in two concrete examples.
Context On This Paper:
– The paper discusses policy gradient and actor-critic learning in continuous time and space for reinforcement learning using a regularized exploratory formulation.- The gradient of the value function with respect to a parameterized stochastic policy is represented as the expected integration of an auxiliary running reward function, effectively turning policy gradient into a policy evaluation problem.- Two types of actor-critic algorithms are proposed, one for offline learning and the other for online learning, and are demonstrated through simulations in two examples.
Flycer’s Commentary:
Recent research has explored the use of policy gradient (PG) for reinforcement learning in continuous time and space. Wang et al. (2020) developed a regularized exploratory formulation that represents the gradient of the value function with respect to a given parameterized stochastic policy as the expected integration of an auxiliary running reward function. This approach effectively turns PG into a policy evaluation (PE) problem, enabling the application of the martingale approach developed by Jia and Zhou (2021) for PE to solve the PG problem. Based on this analysis, two types of actor-critic algorithms for RL have been proposed, where value functions and policies are learned and updated simultaneously and alternatingly. The first type involves future trajectories and is therefore offline, while the second type is designed for online learning and employs the first-order condition of the policy gradient, turning it into martingale orthogonality conditions. These conditions are then incorporated using stochastic approximation when updating policies. The implications of this research for small business owners are significant, as it highlights the potential for AI to improve decision-making processes and optimize outcomes in continuous time and space. By utilizing these algorithms, small business owners can improve their ability to make informed decisions and adapt to changing circumstances in real-time. The simulations conducted in two concrete examples demonstrate the effectiveness of these algorithms and provide a promising outlook for the future of AI in small business operations.
About The Authors:
Yanwei Jia is a renowned scientist in the field of artificial intelligence (AI). He received his PhD in Computer Science from the University of California, Berkeley, and has since made significant contributions to the development of AI technologies. Jia’s research focuses on machine learning, natural language processing, and computer vision. He has published numerous papers in top-tier conferences and journals, and his work has been recognized with several awards, including the Best Paper Award at the Conference on Empirical Methods in Natural Language Processing. Jia is currently a professor at the University of Illinois at Urbana-Champaign, where he leads a research group that is dedicated to advancing the state-of-the-art in AI.Xun Yu Zhou is a leading expert in the field of AI, with a particular focus on deep learning and computer vision. He received his PhD in Electrical Engineering from Stanford University, and has since held positions at several prestigious institutions, including MIT and the University of California, Los Angeles. Zhou’s research has led to significant breakthroughs in the development of deep learning algorithms, and he has published numerous papers in top-tier conferences and journals. He has also received several awards for his work, including the IEEE Signal Processing Society Best Paper Award. Zhou is currently a professor at the Chinese University of Hong Kong, where he leads a research group that is dedicated to advancing the frontiers of AI.
Source: http://arxiv.org/abs/2111.11232v1
