Zap Q-learning is a recent class of reinforcement learning algorithms, motivated primarily as a means to accelerate convergence. Stability theory has been absent outside of two restrictive classes: the tabular setting, and optimal stopping. This paper introduces a new framework for analysis of a more general class of recursive algorithms known as stochastic approximation. Based on this general theory, it is shown that Zap Q-learning is consistent under a non-degeneracy assumption, even when the function approximation architecture is nonlinear. Zap Q-learning with neural network function approximation emerges as a special case, and is tested on examples from OpenAI Gym. Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.

The problem of Offline Policy Evaluation (OPE) in Reinforcement Learning (RL) is a critical step towards applying RL in real-life applications. Existing work on OPE mostly focus on evaluating a fixed target policy π, which does not provide useful bounds for offline policy learning as π will then be data-dependent. We address this problem by simultaneously evaluating all policies in a policy class Π -- uniform convergence in OPE -- and obtain nearly optimal error bounds for a number of global / local policy classes. Our results imply that the model-based planning achieves an optimal episode complexity of O˜(H3/dmϵ2) in identifying an ϵ-optimal policy under the time-inhomogeneous episodic MDP model (H is the planning horizon, dm is a quantity that reflects the exploration of the logging policy μ). To the best of our knowledge, this is the first time the optimal rate is shown to be possible for the offline RL setting and the paper is the first that systematically investigates the uniform convergence in OPE.

In this talk I will discuss recent progress on a long-standing open problem in batch RL: learning Q* from an exploratory and polynomial-sized dataset, using a realizable and otherwise arbitrary function class. In fact, all existing algorithms demand function-approximation assumptions stronger than realizability, and the mounting negative evidence has led to a conjecture that sample-efficient learning is impossible in this setting (Chen and Jiang, 2019). Our algorithm, BVFT, breaks the hardness conjecture (albeit under a stronger notion of exploratory data) via a tournament procedure that reduces the learning problem to pairwise comparison, and solves the latter with the help of a state-action partition constructed from the compared functions. I will also discuss how BVFT can be applied to model selection / holdout validation among other extensions and open problems.

Reinforcement Learning (RL) problems for continuous state and action space systems are among the most challenging in RL. Recently, deep reinforcement learning methods have been shown to be quite effective for certain RL problems in settings of very large/continuous state and action spaces. But such methods require extensive hyper-parameter tuning, huge amount of data, and come with no performance guarantees. We note that such methods are mostly trained `offline’ on experience replay buffers. In this talk, I will describe a series of simple reinforcement learning schemes for various settings. Our premise is that we have access to a generative model that can give us simulated samples of the next state. I will introduce the RANDPOL (randomized function approximation for policy iteration) algorithm, an empirical actor-critic algorithm that uses randomized neural networks that can successfully solve a tough robotic problem with continuous state and action spaces. We also provide theoretical performance guarantees for the algorithm. Specifically, it allows for arbitrarily good approximation with high probability for any problem. I will also touch upon the probabilistic contraction analysis framework of iterative stochastic algorithms that underpins the theoretical analysis. This talk is based on work with Hiteshi Sharma (Microsoft).

In this talk we discuss computational approaches based on Monte Carlo sampling techniques to solving multistage stochastic programming problems. In some applications the considered programs have a periodical behavior. We demonstrate that in such cases it is possible to drastically reduce the number of stages by introducing a periodical analog of the so-called Bellman equations, used in Markov Decision Processes and Stochastic Optimal Control. Furthermore, we describe a primal - dual variant of
the Stochastic Dual Dynamic Programming algorithm, applied to the constructed periodical Bellman equations. We consider risk neutral and risk averse settings.

Robust control theory highlighted the importance of quantifying model uncertainty for the design of feedback control strategies that achieve a provable level of performance. The robustness paradigm motivated work on ‘robust learning’ to address the question of how well model uncertainty can be characterized from data. The quality and convergence rate of model approximation from data imposes a fundamental limit on the rate of adaptation of the underlying control/decision strategy. In particular, for some model classes, sample complexity results impose significant limitations on such adaptation rates. We conjecture that the same limitations exist for convergence in reinforcement learning.
The characterization of the relationship between learning and model uncertainty hinges on having a tractable theory for model approximation. Such a theory exists for broad classes of linear stochastic dynamical systems. In this talk, I will present some results for learning classes of stochastic systems, namely, jump linear systems. A key question for such models is the unraveling of the underlying model structure from data. In this context, I will show that spectral methods allow for estimating both the underlying state dimension when the number of stochastic transitions is known. Utilizing existing results from model reduction using Hankel-like matrices, I will show that efficient learning of low dimensional models is quite possible.
This work is in collaboration with Tuhin Sarkar and Alexander Rakhlin.

The area of offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help alleviate the excessive sample complexity burden in modern sequential decision making problems. As such, the approach is becoming increasingly important to numerous areas of science, engineering, and technology. However, the extent to which this broader approach can be effective is not well understood, where the literature largely consists of sufficient conditions.
This work focuses on the basic question of what are necessary representational and distributional conditions that permit provable sample-efficient offline reinforcement learning. Perhaps surprisingly, our main result shows that even if: i) we have realizability in that the true value function of _every_ policy is linear in a given set of features and 2) our off-policy data has good coverage over all features (under a strong spectral condition), then any algorithm still (information-theoretically) requires a number of offline samples that is exponential in the problem horizon in order to non-trivially estimate the value of _any_ given policy. Our results highlight that sample-efficient, offline policy evaluation is simply not possible unless significantly stronger conditions hold; such conditions include either having low distribution shift (where the offline data distribution is close to the distribution of the policy to be evaluated) or significantly stronger representational conditions (beyond realizability).
This is joint work with Ruosong Wang and Dean Foster.

A softmax operator applied to a set of values acts somewhat like the maximization function and somewhat like an average. In sequential decision making, softmax is often used in settings where it is necessary to maximize utility but also to hedge against problems that arise from putting all of one's weight behind a single maximum utility decision. The Boltzmann softmax operator is the most commonly used softmax operator in this setting, but we show that this operator is prone to misbehavior. In this work, we study a differentiable softmax operator that, among other properties, is a non-expansion ensuring a convergent behavior in learning and planning. We introduce a variant of SARSA algorithm that, by utilizing the new operator, computes a Boltzmann policy with a state-dependent temperature parameter. We show that the algorithm is convergent and that it performs favorably in practice. (With Kavosh Asadi.)

Policy gradients methods apply to complex, poorly understood, control problems by performing stochastic gradient descent over a parameterized class of polices. Unfortunately, due to the multi-period nature of the objective, policy gradient algorithms face non-convex optimization problems and can get stuck in suboptimal local minima even for extremely simple problems. This talk with discus structural properties ‚Äì shared by several canonical control problems ‚Äì that guarantee the policy gradient objective function has no suboptimal stationary points despite being non-convex. Time permitting, I‚Äôll then zoom in on the special case of state aggregated policies and a proof showing that policy gradient converges to better policies than its relative, approximate policy iteration.