The thirty-seventh annual Neural Information Processing Systems (NeurIPS) conference begins in New Orleans this week, and Amii is proud to share some of the research that our Fellows, Canada CIFAR AI Chairs and affiliated students are presenting at this year's event.

First started in 1987, NeurIPS has grown into a premier conference on machine learning and cognitive neuroscience. Every year, it draws researchers from many different disciplines, including information theory, computer vision and linguistics.

Below are 16 papers co-authored by Amii Fellows, Canada CIFAR AI Chairs and early-stage researchers that were accepted at NeurIPS.

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General Munchausen Reinforcement Learning with Tsallis Kullback-Leibler Divergence

Lingwei Zhu, Zheng Chen, Matthew Schlegel, *Martha White

Abstract: Many policy optimization approaches in reinforcement learning incorporate a Kullback-Leilbler (KL) divergence to the previous policy, to prevent the policy from changing too quickly. This idea was initially proposed in a seminal paper on Conservative Policy Iteration, with approximations given by algorithms like TRPO and Munchausen Value Iteration (MVI). We continue this line of work by investigating a generalized KL divergence—called the Tsallis KL divergence. Tsallis KL defined by the q-logarithm is a strict generalization, as q = 1 corresponds to the standard KL divergence; q > 1 provides a range of new options. We characterize the types of policies learned under the Tsallis KL, and motivate when q > 1 could be beneficial. To obtain a practical algorithm that incorporates Tsallis KL regularization, we extend MVI, which is one of the simplest approaches to incorporate KL regularization. We show that this generalized MVI(q) obtains significant improvements over the standard MVI(q = 1) across 35 Atari games.

Utilitarian Algorithm Configuration

Devon Graham, *Kevin Leyton-Brown, Tim Roughgarden

Abstract:

We present the first nontrivial procedure for configuring heuristic algorithms to maximize the utility provided to their end users while also offering theoretical guarantees about performance. Existing procedures seek configurations that minimize expected runtime. However, very recent theoretical work argues that expected runtime minimization fails to capture algorithm designers’ preferences. Here we show that the utilitarian objective also confers significant algorithmic benefits. Intuitively, this is because mean runtime is dominated by extremely long runs even when they are incredibly rare; indeed, even when an algorithm never gives rise to such long runs, configuration procedures that provably minimize mean runtime must perform a huge number of experiments to demonstrate this fact. In contrast, utility is bounded and monotonically decreasing in runtime, allowing for meaningful empirical bounds on a configuration’s performance. This paper builds on this idea to describe effective and theoretically sound configuration procedures. We prove upper bounds on the runtime of these procedures that are similar to theoretical lower bounds, while also demonstrating their performance empirically.

Don't be so Monotone: Relaxing Stochastic Line Search in Over-Parameterized Models

Leonardo Galli, Holger Rauhut, *Mark Schmidt

Abstract:

Recent works have shown that line search methods can speed up Stochastic Gradient Descent (SGD) and Adam in modern over-parameterized settings. However, existing line searches may take steps that are smaller than necessary since they require a monotone decrease of the (mini-)batch objective function. We explore nonmonotone line search methods to relax this condition and possibly accept larger step sizes. Despite the lack of a monotonic decrease, we prove the same fast rates of convergence as in the monotone case. Our experiments show that nonmonotone methods improve the speed of convergence and generalization properties of SGD/Adam even beyond the previous monotone line searches. We propose a POlyak NOnmonotone Stochastic (PoNoS) method, obtained by combining a nonmonotone line search with a Polyak initial step size. Furthermore, we develop a new resetting technique that in the majority of the iterations reduces the amount of backtracks to zero while still maintaining a large initial step size. To the best of our knowledge, a first runtime comparison shows that the epoch-wise advantage of line-search-based methods gets reflected in the overall computational time.

Searching for Optimal Per-Coordinate Step-sizes with Multidimensional Backtracking

Frederik Kunstner, Victor Sanches Portella, *Mark Schmidt, Nicholas Harvey

Abstract:

The backtracking step size is an effective technique to automatically tune the step-size in smooth optimization. It guarantees similar performance to using the theoretically optimal step-size. Many approaches have been developed to instead tune per-coordinate step-sizes, also known as diagonal preconditioners, but none of the existing methods are provably competitive with the optimal per-coordinate stepsizes. We propose multidimensional backtracking, an extension of the backtracking line-search to find good diagonal preconditioners for smooth convex problems. Our key insight is that the gradient with respect to the step-sizes, also known as hypergradients, yields separating hyperplanes that let us search for good preconditioners using cutting-plane methods. As black-box cutting-plane approaches like the ellipsoid method are computationally prohibitive, we develop an efficient algorithm tailored to our setting. Multidimensional backtracking is provably competitive with the best diagonal preconditioner and requires no manual tuning.

Learning Universal Policies via Text-Guided Video Generation

Yilun Du, Sherry Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Josh Tenenbaum, *Dale Schuurmans, Pieter Abbeel

Abstract:

A line searchgoal of artificial intelligence is to construct an agent that can solve a wide variety of tasks. Recent progress in text-guided image synthesis has yielded models with an impressive ability to generate complex novel images, exhibiting combinatorial generalization across domains. Motivated by this success, we investigate whether such tools can be used to construct more general-purpose agents. Specifically, we cast the sequential decision making problem as a text-conditioned video generation problem, where, given a text-encoded specification of a desired goal, a planner synthesizes a set of future frames depicting its planned actions in the future, after which control actions are extracted from the generated video. By leveraging text as the underlying goal specification, we are able to naturally and combinatorially generalize to novel goals. The proposed policy-as-video formulation can further represent environments with different state and action spaces in a unified space of images, which, for example, enables learning and generalization across a variety of robot manipulation tasks. Finally, by leveraging pretrained language embeddings and widely available videos from the internet, the approach enables knowledge transfer through predicting highly realistic video plans for real robots.

Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off

Zichen Zhang, Johannes Kirschner, Junxi Zhang, Francesco Zanini, Alex Ayoub, Masood Dehghan, *Dale Schuurmans,

Abstract:

A default assumption in reinforcement learning (RL) and optimal control is that observations arrive at discrete time points on a fixed clock cycle. Yet, many applications involve continuous-time systems where the time discretization, in principle, can be managed. The impact of time discretization on RL methods has not been fully characterized in existing theory, but a more detailed analysis of its effect could reveal opportunities for improving data-efficiency. We address this gap by analyzing Monte-Carlo policy evaluation for LQR systems and uncover a fundamental trade-off between approximation and statistical error in value estimation. Importantly, these two errors behave differently to time discretization, leading to an optimal choice of temporal resolution for a given data budget. These findings show that managing the temporal resolution can provably improve policy evaluation efficiency in LQR systems with finite data. Empirically, we demonstrate the trade-off in numerical simulations of LQR instances and standard RL benchmarks for non-linear continuous control.

Ordering-based Conditions for Global Convergence of Policy Gradient Methods

Jincheng Mei, Bo Dai, Alekh Agarwal, Mohammad Ghavamzadeh, Csaba Szepesvari, Dale Schuurmans

Abstract:

We prove that, for finite-arm bandits with linear function approximation, the global convergence of policy gradient (PG) methods depends on inter-related properties between the policy update and the representation. First, we establish a few key observations that frame the study: Global convergence can be achieved under linear function approximation without policy or reward realizability, both for the standard Softmax PG and natural policy gradient (NPG). Approximation error is not a key quantity for characterizing global convergence in either algorithm. The conditions on the representation that imply global convergence are different between these two algorithms. Overall, these observations call into question approximation error as an appropriate quantity for characterizing the global convergence of PG methods under linear function approximation. \Second, motivated by these observations, we establish new general results: NPG with linear function approximation achieves global convergence if and only if the projection of the reward onto the representable space preserves the optimal action's rank, a quantity that is not strongly related to approximation error. The global convergence of Softmax PG occurs if the representation satisfies a non-domination condition and can preserve the ranking of rewards, which goes well beyond policy or reward realizability. We provide experimental results to support these theoretical findings.

DISCS: A Benchmark for Discrete Sampling

Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Grathwohl, *Dale Schuurmans, Hanjun Dai

Abstract:

Sampling in discrete spaces, with critical applications in simulation and optimization, has recently been boosted by significant advances in gradient-based approaches that exploit modern accelerators like GPUs. However, two key challenges are hindering further advancement in research on discrete sampling. First, since there is no consensus on experimental settings and evaluation setups, the empirical results in different research papers are often not comparable. Second, implementing samplers and target distributions often requires a nontrivial amount of effort in terms of calibration and parallelism. To tackle these challenges, we propose DISCS (DISCrete Sampling), a tailored package and benchmark that supports unified and efficient experiment implementation and evaluations for discrete sampling in three types of tasks: sampling from classical graphical models and energy based generative models, and sampling for solving combinatorial optimization. Throughout the comprehensive evaluations in DISCS, we gained new insights into scalability, design principles for proposal distributions, and lessons for adaptive sampling design. DISCS efficiently implements representative discrete samplers in existing research works as baselines and offers a simple interface that researchers can conveniently add new discrete samplers and directly compare their performance with the benchmark result in a calibrated setup.

History Filtering in Imperfect Information Games: Algorithms and Complexity

Christopher Solinas, Doug Rebstock, *Nathan Sturtevant, Michael Buro

Abstract:

Historically applied exclusively to perfect information games, depth-limited search with value functions has been key to recent advances in AI for imperfect information games. Most prominent approaches with strong theoretical guarantees require subgame decomposition - a process in which a subgame is computed from public information and player beliefs. However, subgame decomposition can itself require non-trivial computations, and its tractability depends on the existence of efficient algorithms for either full enumeration or generation of the histories that form the root of the subgame. Despite this, no formal analysis of the tractability of such computations has been established in prior work, and application domains have often consisted of games, such as poker, for which enumeration is trivial on modern hardware. Applying these ideas to more complex domains requires understanding their cost.

In this work, we introduce and analyze the computational aspects and tractability of filtering histories for subgame decomposition. We show that constructing a single history from the root of the subgame is generally intractable, and then provide a necessary and sufficient condition for efficient enumeration. We also introduce a novel Markov Chain Monte Carlo-based generation algorithm for trick-taking card games - a domain where enumeration is often prohibitively expensive. Our experiments demonstrate its improved scalability in the trick-taking card game Oh Hell. These contributions clarify when and how depth-limited search via subgame decomposition can be an effective tool for sequential decision-making in imperfect information settings.

Improving Compositional Generalization using Iterated Learning and Simplicial Embeddings

Yi Ren, Samuel Lavoie, Michael Galkin, *Danica J. Sutherland, Aaron Courville

Abstract:

Compositional generalization, the ability of an agent to generalize to unseen combinations of latent factors, is easy for humans but hard for deep neural networks. A line of research in cognitive science has hypothesized a process, ``iterated learning,'' to help explain how human language developed this ability; the theory rests on simultaneous pressures towards compressibility (when an ignorant agent learns from an informed one) and expressivity (when it uses the representation for downstream tasks). Inspired by this process, we propose to improve the compositional generalization of deep networks by using iterated learning on models with simplicial embeddings, which can approximately discretize representations. This approach is further motivated by an analysis of compositionality based on Kolmogorov complexity. We show that this combination of changes improves compositional generalization over other approaches, demonstrating these improvements both on vision tasks with well-understood latent factors and on real molecular graph prediction tasks where the latent structure is unknown.

Online RL in Linearly q^\pi-Realizable MDPs Is as Easy as in Linear MDPs If You Learn What to Ignore

Gellert Weisz, András György, *Csaba Szepesvari

Abstract:

We consider online reinforcement learning (RL) in episodic Markov decision processes (MDPs) under the linear q^\pi -realizability assumption, where it is assumed that the action-values of all policies can be expressed as linear functions of state action features. This class is known to be more general than linear MDPs, where the transition kernel and the reward function are assumed to be linear functions of the feature vectors. As our first contribution, we show that the difference between the two classes is the presence of states in linearly q^\pi -realizable MDPs where for any policy, all the actions have approximately equal values, and skipping over these states by following an arbitrarily fixed policy in those states transforms the problem to a linear MDP. Based on this observation, we derive a novel (computationally inefficient) learning algorithm for linearly q^\pi -realizable MDPs that simultaneously learns what states should be skipped over and runs another learning algorithm on the linear MDP hidden in the problem. The method returns an epsilon-optimal policy after polylog(H,d)epsilon^2 interactions with the MDP, where H is the time horizon and d is the dimension of the feature vectors, giving the first polynomial-sample-complexity online RL algorithm for this setting. The results are proved for the misspecified case, where the sample complexity is shown to degrade gracefully with the misspecification error.

Regret Minimization via Saddle Point Optimization

Johannes Kirschner, Alireza Bakhtiari, Kushagra Chandak, Volodymyr Tkachuk,

Abstract:

A long line of works characterizes the sample complexity of regret minimization in sequential decision-making by min-max programs. In the corresponding saddle-point game, the min-player optimizes the sampling distribution against an adversarial max-player that chooses confusing models leading to large regret. The most recent instantiation of this idea is the decision-estimation coefficient (DEC), which was shown to provide nearly tight lower and upper bounds on the worst-case expected regret in structured bandits and reinforcement learning. By re-parametrizing the offset DEC with the confidence radius and solving the corresponding min-max program, we derive an anytime variant of the Estimation-To-Decisions algorithm (Anytime-E2D). Importantly, the algorithm optimizes the exploration-exploitation trade-off online instead of via the analysis. Our formulation leads to a practical algorithm for finite model classes and linear feedback models. We illustrate the results by deriving improved rates for high-dimensional linear bandits. Lastly, we point out connections to the information ratio, decoupling coefficient and PAC-DEC, and numerically evaluate the performance of E2D on simple examples.

Context-lumpable stochastic bandits

Chung-Wei Lee, Qinghua Liu, Yasin Abbasi Yadkori, Chi Jin, Tor Lattimore, *Csaba Szepesvari

Abstract:

We consider a contextual bandit problem with S contexts and K actions. In each round t = 1, 2 ... the learner observes a random context and chooses an action based on its past experience. The learner then observes a random reward whose mean is a function of the context and the action for the round. Under the assumption that the contexts can be lumped into r < min (K, S) groups such that the mean reward for the various actions is the same for any two contexts that are in the same group, we give an algorithm that outputs an epsilon-optimal policy after using at most O~(sqrt{(r (S +K )/epsilon^2)} samples with high probability and provide a matching Omega(r (S +K )/epsilon^2) lower bound. In the regret minimization setting, we give an algorithm whose cumulative regret up to time T is bounded by O~(\sqrt{r ^3(S +K )T})$ To the best of our knowledge, we are the first to show the near-optimal sample complexity in the PAC setting and O~{\sqrt{\text{poly}(r)(S+K)T}} minimax regret in the online setting for this problem. We also show our algorithms can be applied to more general low-rank bandits and get improved regret bounds in some scenarios.

Optimistic Natural Policy Gradient: a Simple Efficient Policy Optimization Framework for Online RL

Qinghua Liu, Gellert Weisz, András György, Chi Jin, *Csaba Szepesvari

Abstract:

While policy optimization algorithms have played an important role in recent empirical success of Reinforcement Learning (RL), the existing theoretical understanding of policy optimization remains rather limited—they are either restricted to tabular MDPs or suffer from highly suboptimal sample complexity, especially in online RL where exploration is necessary. This paper proposes a simple efficient policy optimization framework—OPTIMISTIC NPG for online RL. OPTIMISTIC NPG can be viewed as a simple combination of the classic natural policy gradient (NPG) algorithm [Kakade, 2001] and an optimistic policy evaluation subroutine to encourage exploration. For d-dimensional linear MDPs, OPTIMISTIC NPG is computationally efficient, and learns an ϵ-optimal policy within O˜(d 2/ϵ3 ) samples, which is the first computationally efficient algorithm whose sample complexity has the optimal dimension dependence Θ( ˜ d 2 ). It also improves over state-of-the-art results of policy optimization algorithms [Zanette et al., 2021] by a factor of d. For general function approximation that subsumes linear MDPs, OPTIMISTIC NPG, to our best knowledge, is also the first policy optimization algorithm that achieves the polynomial sample complexity for learning near-optimal policies.

Ignorance is Bliss: Robust Control via Information Gating

Manan Tomar, Riashat Islam, Matthew E. Taylor, Sergey Levine, Philip Bachman

Abstract:

Informational parsimony -- i.e., using the minimal information required for a task, -- provides a useful inductive bias for learning representations that achieve better generalization by being robust to noise and spurious correlations. We propose information gating in the pixel space as a way to learn more parsimonious representations. Information gating works by learning masks that capture only the minimal information required to solve a given task. Intuitively, our models learn to identify which visual cues actually matter for a given task. We gate information using a differentiable parameterization of the signal-to-noise ratio, which can be applied to arbitrary values in a network, e.g.~masking out pixels at the input layer. We apply our approach, which we call InfoGating, to various objectives such as: multi-step forward and inverse dynamics, Q-learning, behavior cloning, and standard self-supervised tasks. Our experiments show that learning to identify and use minimal information can improve generalization in downstream tasks -- e.g., policies based on info-gated images are considerably more robust to distracting/irrelevant visual features.

Guarantees for Self-Play in Multiplayer Games via Polymatrix Decomposability

Revan MacQueen, James Wright

Abstract:

Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself. Self-play is useful for generating large quantities of data for learning, but has the drawback that the agents the learner will face post-training may have dramatically different behavior than the learner came to expect by interacting with itself. For the special case of two-player constant-sum games, self-play that reaches Nash equilibrium is guaranteed to produce strategies that perform well against any post-training opponent; however, no such guarantee exists for multi-player games. We show that in games that approximately decompose into a set of two-player constant-sum games (called polymatrix games) where global ϵ-Nash equilibria are boundedly far from Nash-equilibria in each subgame, any no-external-regret algorithm that learns by self-play will produce a strategy with bounded vulnerability. For the first time, our results identify a structural property of multi-player games that enable performance guarantees for the strategies produced by a broad class of self-play algorithms. We demonstrate our findings through experiments on Leduc poker.