Neural Policy Gradient Methods: Global Optimality and Rates of Convergence

Policy gradient methods with actor-critic schemes demonstrate tremendous empirical successes, especially when the actors and critics are parameterized by neural networks. However, it remains less clear whether such "neural" policy gradient methods converge to globally optimal policies and whether they even converge at all. We answer both the questions affirmatively in the overparameterized regime. In detail, we prove that neural natural policy gradient converges to a globally optimal policy at a sublinear rate. Also, we show that neural vanilla policy gradient converges sublinearly to a stationary point. Meanwhile, by relating the suboptimality of the stationary points to the representation power of neural actor and critic classes, we prove the global optimality of all stationary points under mild regularity conditions. Particularly, we show that a key to the global optimality and convergence is the "compatibility" between the actor and critic, which is ensured by sharing neural architectures and random initializations across the actor and critic. To the best of our knowledge, our analysis establishes the first global optimality and convergence guarantees for neural policy gradient methods.

Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization

We consider a distributed multi-agent policy evaluation problem in reinforcement learning. In our setup, a group of agents with jointly observed states and private local actions and rewards collaborates to learn the value function of a given policy. When the dimension of state-action space is large, the temporal-difference learning with linear function approximation is widely used. Under the assumption that the samples are i.i.d., the best-known convergence rate for multi-agent temporal-difference learning is $O(1/\sqrt{T})$ minimizing the mean square projected Bellman error. In this paper, we formulate the temporal-difference learning as a distributed stochastic saddle point problem, and propose a new homotopy primal-dual algorithm by adaptively restarting the gradient update from the average of previous iterations. We prove that our algorithm enjoys an $O(1/T)$ convergence rate up to logarithmic factors of $T$, thereby significantly improving the previously-known convergence results on multi-agent temporal-difference learning. Furthermore, since our result explicitly takes into account the Markovian nature of the sampling in policy evaluation, it addresses a broader class of problems than the commonly used i.i.d. sampling scenario. From a stochastic optimization perspective, to the best of our knowledge, the proposed homotopy primal-dual algorithm is the first to achieve $O(1/T)$ convergence rate for distributed stochastic saddle point problem.

Robust One-Bit Recovery via ReLU Generative Networks: Improved Statistical Rates and Global Landscape Analysis

We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector $\theta_0\in\mathbb{R}^d$ uniformly $m$ quantized noisy measurements. Under the assumption that the measurements are sub-Gaussian random vectors, to recover any $k$-sparse $\theta_0$ ($k\ll d$) uniformly up to an error $\varepsilon$ with high probability, the best known computationally tractable algorithm requires $m\geq\tilde{\mathcal{O}}(k\log d/\varepsilon^4)$ measurements. In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0 \in \mathbb{R}^k$ through a known $n$-layer ReLU generative network $G:\mathbb{R}^k\rightarrow\mathbb{R}^d$. Such a framework poses low-dimensional priors on $\theta_0$ without a known basis. We propose to recover the target $G(x_0)$ via an unconstrained empirical risk minimization (ERM) problem under a much weaker sub-exponential measurement assumption. For such a problem, we establish a joint statistical and computational analysis. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of $m=\tilde{\mathcal{O}}(kn \log d /\varepsilon^2)$ recovering any $G(x_0)$ uniformly up to an error $\varepsilon$. Moreover, from the lens of computation, despite non-convexity, we prove that the objective of our ERM problem has no spurious stationary point, that is, any stationary point are equally good for recovering the true target up to scaling with a certain accuracy. Furthermore, our analysis also shed lights on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.

Provably Efficient Reinforcement Learning with Linear Function Approximation

Modern Reinforcement Learning (RL) is commonly applied to practical problems with an enormous number of states, where function approximation must be deployed to approximate either the value function or the policy. The introduction of function approximation raises a fundamental set of challenges involving computational and statistical efficiency, especially given the need to manage the exploration/exploitation tradeoff. As a result, a core RL question remains open: how can we design provably efficient RL algorithms that incorporate function approximation? This question persists even in a basic setting with linear dynamics and linear rewards, for which only linear function approximation is needed. This paper presents the first provable RL algorithm with both polynomial runtime and polynomial sample complexity in this linear setting, without requiring a "simulator" or additional assumptions. Concretely, we prove that an optimistic modification of Least-Squares Value Iteration (LSVI)---a classical algorithm frequently studied in the linear setting---achieves $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret, where $d$ is the ambient dimension of feature space, $H$ is the length of each episode, and $T$ is the total number of steps. Importantly, such regret is independent of the number of states and actions.

Fast multi-agent temporal-difference learning via homotopy stochastic primal-dual optimization

We consider a distributed multi-agent policy evaluation problem in reinforcement learning. In our setup, a group of agents with jointly observed states and private local actions and rewards collaborates to learn the value function of a given policy. When the dimension of state-action space is large, the temporal-difference learning with linear function approximation is widely used. Under the assumption that the samples are i.i.d., the best-known convergence rate for multi-agent temporal-difference learning is $O(1/\sqrt{T})$ minimizing the mean square projected Bellman error. In this paper, we formulate the temporal-difference learning as a distributed stochastic saddle point problem, and propose a new homotopy primal-dual algorithm by adaptively restarting the gradient update from the average of previous iterations. We prove that our algorithm enjoys an $O(1/T)$ convergence rate up to logarithmic factors of $T$, thereby significantly improving the previously-known convergence results on multi-agent temporal-difference learning. Furthermore, since our result explicitly takes into account the Markovian nature of the sampling in policy evaluation, it addresses a broader class of problems than the commonly used i.i.d. sampling scenario. From a stochastic optimization perspective, to the best of our knowledge, the proposed homotopy primal-dual algorithm is the first to achieve $O(1/T)$ convergence rate for distributed stochastic saddle point problem.

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning

We consider the weakly supervised binary classification problem where the labels are randomly flipped with probability $1- {\alpha}$. Although there exist numerous algorithms for this problem, it remains theoretically unexplored how the statistical accuracies and computational efficiency of these algorithms depend on the degree of supervision, which is quantified by ${\alpha}$. In this paper, we characterize the effect of ${\alpha}$ by establishing the information-theoretic and computational boundaries, namely, the minimax-optimal statistical accuracy that can be achieved by all algorithms, and polynomial-time algorithms under an oracle computational model. For small ${\alpha}$, our result shows a gap between these two boundaries, which represents the computational price of achieving the information-theoretic boundary due to the lack of supervision. Interestingly, we also show that this gap narrows as ${\alpha}$ increases. In other words, having more supervision, i.e., more correct labels, not only improves the optimal statistical accuracy as expected, but also enhances the computational efficiency for achieving such accuracy.

On the Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost

Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating update algorithm for bilevel optimization, whose convergence is known to be fragile. To understand the instability of actor-critic, we focus on its application to linear quadratic regulators, a simple yet fundamental setting of reinforcement learning. We establish a nonasymptotic convergence analysis of actor-critic in this setting. In particular, we prove that actor-critic finds a globally optimal pair of actor (policy) and critic (action-value function) at a linear rate of convergence. Our analysis may serve as a preliminary step towards a complete theoretical understanding of bilevel optimization with nonconvex subproblems, which is NP-hard in the worst case and is often solved using heuristics.

Stochastic Convergence Results for Regularized Actor-Critic Methods

In this paper, we present a stochastic convergence proof, under suitable conditions, of a certain class of actor-critic algorithms for finding approximate solutions to entropy-regularized MDPs using the machinery of stochastic approximation. To obtain this overall result, we provide three fundamental results that are all of both practical and theoretical interest: we prove the convergence of policy evaluation with general regularizers when using linear approximation architectures, we derive an entropy-regularized policy gradient theorem, and we show convergence of entropy-regularized policy improvement. We also provide a simple, illustrative empirical study corroborating our theoretical results. To the best of our knowledge, this is the first time such results have been provided for approximate solution methods for regularized MDPs.

A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning

This paper considers a distributed reinforcement learning problem in which a network of multiple agents aim to cooperatively maximize the globally averaged return through communication with only local neighbors. A randomized communication-efficient multi-agent actor-critic algorithm is proposed for possibly unidirectional communication relationships depicted by a directed graph. It is shown that the algorithm can solve the problem for strongly connected graphs by allowing each agent to transmit only two scalar-valued variables at one time.