Finite-Time Analysis of Entropy-Regularized Neural Natural Actor-Critic Algorithm
Authors: Semih Cayci, Niao He, R. Srikant
TMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present a finite-time analysis of NAC with neural network approximation, and identify the roles of neural networks, regularization and optimization techniques (e.g., gradient clipping and weight decay) to achieve provably good performance in terms of sample complexity, iteration complexity and overparametrization bounds for the actor and the critic. In particular, we prove that (i) entropy regularization and weight decay ensure stability by providing sufficient exploration to avoid near-deterministic and strictly suboptimal policies and (ii) regularization leads to sharp sample complexity and network width bounds in the regularized MDPs, yielding a favorable bias-variance tradeoff in policy optimization. In the process, we identify the importance of uniform approximation power of the actor neural network to achieve global optimality in policy optimization due to distributional shift. |
| Researcher Affiliation | Academia | Semih Cayci EMAIL Department of Mathematics RWTH Aachen University Niao He EMAIL Department of Computer Science ETH Zurich R. Srikant EMAIL ECE and CSL University of Illinois at Urbana-Champaign |
| Pseudocode | Yes | Algorithm 1: sym_init(m, d) Symmetric Initialization Algorithm 2: Entropy-regularized Neural NAC Algorithm 3: MN-NTD Max-Norm Regularized Neural TD Learning Algorithm 4: Sampler from dπ µ π under a generative model |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. It only mentions the paper being "Reviewed on Open Review: https: // openreview. net/ forum? id= Bk Eqk7p S1I" which is a review platform. |
| Open Datasets | No | The paper focuses on theoretical analysis of reinforcement learning algorithms within Markov Decision Processes (MDPs). It discusses abstract 'state and action spaces' and 'unknown and dynamical environments' but does not refer to any specific publicly available datasets with access information (link, DOI, or citation to a dataset paper). |
| Dataset Splits | No | The paper is theoretical and does not present experiments run on specific datasets. Therefore, there is no discussion or specification of training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is purely theoretical, focusing on the mathematical analysis and convergence bounds of an algorithm. It does not describe any experimental setup or report empirical results that would necessitate the use or specification of hardware. |
| Software Dependencies | No | The paper is theoretical and does not present any empirical experiments. Consequently, it does not specify any software dependencies with version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | The paper is a theoretical analysis of an algorithm and does not include an experimental section. Therefore, no details regarding hyperparameters, training configurations, or system-level settings for experiments are provided. |