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Andrew Patterson

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16 papers
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16

AAMAS Conference 2026 Conference Paper

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?

  • Vincent Liu
  • Prabhat Nagarajan
  • Andrew Patterson
  • Martha White

Offline reinforcement learning algorithms often require careful hyperparametertuning. Beforedeployment, weneedtoselectamongst a set of candidate policies. However, there is limited understanding about the fundamental limits of this offline policy selection (OPS) problem. In this work we provide clarity on when sample efficient OPS is possible, primarily by connecting OPS to off-policy policy evaluation (OPE) and Bellman error (BE) estimation. We first show a hardness result, that in the worst case, OPS is just as hard as OPE, by proving a reduction of OPE to OPS. As a result, no OPS method can be more sample efficient than OPE in the worst case. We then connect BE estimation to the OPS problem, showing how BEcanbeusedasatoolforOPS. WhileBE-basedmethodsgenerally require stronger requirements than OPE, when those conditions are met they can be more sample efficient. Building on this insight, we propose a BE method for OPS, called Identifiable BE Selection (IBES), that has a straightforward method for selecting its own hyperparameters. We conclude with an empirical study comparing OPE and IBES, and by showing the difficulty of OPS on an offline Atari benchmark dataset.

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RLJ Journal 2025 Journal Article

Deep Reinforcement Learning with Gradient Eligibility Traces

  • Esraa Elelimy
  • Brett Daley
  • Andrew Patterson
  • Marlos C. Machado
  • Adam White
  • Martha White

Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are consequently susceptible to divergence. While more principled approaches like Gradient TD (GTD) methods have strong convergence guarantees, they have rarely been used in deep RL. Recent work introduced the generalized Projected Bellman Error ($\overline{\text{PBE}}$), enabling GTD methods to work efficiently with nonlinear function approximation. However, this work is limited to one-step methods, which are slow at credit assignment and require a large number of samples. In this paper, we extend the generalized $\overline{\text{PBE}}$ objective to support multistep credit assignment based on the $\lambda$-return and derive three gradient-based methods that optimize this new objective. We provide both a forward-view formulation compatible with experience replay and a backward-view formulation compatible with streaming algorithms. Finally, we evaluate the proposed algorithms and show that they outperform both PPO and StreamQ in MuJoCo and MinAtar environments, respectively.

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RLC Conference 2025 Conference Paper

Deep Reinforcement Learning with Gradient Eligibility Traces

  • Esraa Elelimy
  • Brett Daley
  • Andrew Patterson
  • Marlos C. Machado
  • Adam White
  • Martha White

Achieving fast and stable off-policy learning in deep reinforcement learning (RL) is challenging. Most existing methods rely on semi-gradient temporal-difference (TD) methods for their simplicity and efficiency, but are consequently susceptible to divergence. While more principled approaches like Gradient TD (GTD) methods have strong convergence guarantees, they have rarely been used in deep RL. Recent work introduced the generalized Projected Bellman Error ($\overline{\text{PBE}}$), enabling GTD methods to work efficiently with nonlinear function approximation. However, this work is limited to one-step methods, which are slow at credit assignment and require a large number of samples. In this paper, we extend the generalized $\overline{\text{PBE}}$ objective to support multistep credit assignment based on the $\lambda$-return and derive three gradient-based methods that optimize this new objective. We provide both a forward-view formulation compatible with experience replay and a backward-view formulation compatible with streaming algorithms. Finally, we evaluate the proposed algorithms and show that they outperform both PPO and StreamQ in MuJoCo and MinAtar environments, respectively.

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RLC Conference 2024 Conference Paper

Cross-environment Hyperparameter Tuning for Reinforcement Learning

  • Andrew Patterson
  • Samuel Neumann
  • Raksha Kumaraswamy
  • Martha White
  • Adam White

This paper introduces a new benchmark, the Cross-environment Hyperparameter Setting Benchmark, that allows comparison of RL algorithms across environments using only a single hyperparameter setting, encouraging algorithmic development which is insensitive to hyperparameters. We demonstrate that the benchmark is robust to statistical noise and obtains qualitatively similar results across repeated applications, even when using a small number of samples. This robustness makes the benchmark computationally cheap to apply, allowing statistically sound insights at low cost. We provide two example instantiations of the CHS, on a set of six small control environments (SC-CHS) and on the entire DM Control suite of 28 environments (DMC-CHS). Finally, to demonstrate the applicability of the CHS to modern RL algorithms on challenging environments, we provide a novel empirical study of an open question in the continuous control literature. We show, with high confidence, that there is no meaningful difference in performance between Ornstein-Uhlenbeck noise and uncorrelated Gaussian noise for exploration with the DDPG algorithm on the DMC-CHS.

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RLJ Journal 2024 Journal Article

Cross-environment Hyperparameter Tuning for Reinforcement Learning

  • Andrew Patterson
  • Samuel Neumann
  • Raksha Kumaraswamy
  • Martha White
  • Adam White

This paper introduces a new benchmark, the Cross-environment Hyperparameter Setting Benchmark, that allows comparison of RL algorithms across environments using only a single hyperparameter setting, encouraging algorithmic development which is insensitive to hyperparameters. We demonstrate that the benchmark is robust to statistical noise and obtains qualitatively similar results across repeated applications, even when using a small number of samples. This robustness makes the benchmark computationally cheap to apply, allowing statistically sound insights at low cost. We provide two example instantiations of the CHS, on a set of six small control environments (SC-CHS) and on the entire DM Control suite of 28 environments (DMC-CHS). Finally, to demonstrate the applicability of the CHS to modern RL algorithms on challenging environments, we provide a novel empirical study of an open question in the continuous control literature. We show, with high confidence, that there is no meaningful difference in performance between Ornstein-Uhlenbeck noise and uncorrelated Gaussian noise for exploration with the DDPG algorithm on the DMC-CHS.

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JMLR Journal 2024 Journal Article

Empirical Design in Reinforcement Learning

  • Andrew Patterson
  • Samuel Neumann
  • Martha White
  • Adam White

Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute resources available per dollar have continued to grow rapidly, so have the scale of typical experiments in reinforcement learning. It is now common to benchmark agents with millions of parameters against dozens of tasks, each using the equivalent of 30 days of experience. The scale of these experiments often conflict with the need for statistical evidence, especially when comparing algorithms. Recent studies have highlighted how popular algorithms are sensitive to hyperparameter settings and implementation details, and that common empirical practice leads to weak statistical evidence (Machado et al., 2018; Henderson et al., 2018). This manuscript represents both a call to action, and a comprehensive resource for how to do good experiments in reinforcement learning. In particular, we cover: the statistical assumptions underlying common performance measures, how to properly characterize performance variation and stability, hypothesis testing, special considerations for comparing multiple agents, baseline and illustrative example construction, and how to deal with hyperparameters and experimenter bias. Throughout we highlight common mistakes found in the literature and the statistical consequences of those in example experiments. The objective of this document is to provide answers on how we can use our unprecedented compute to do good science in reinforcement learning, as well as stay alert to potential pitfalls in our empirical design. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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RLC Conference 2024 Conference Paper

Investigating the Interplay of Prioritized Replay and Generalization

  • Parham Mohammad Panahi
  • Andrew Patterson
  • Martha White
  • Adam White

Experience replay, the reuse of past data to improve sample efficiency, is ubiquitous in reinforcement learning. Though a variety of smart sampling schemes have been introduced to improve performance, uniform sampling by far remains the most common approach. One exception is Prioritized Experience Replay (PER), where sampling is done proportionally to TD errors, inspired by the success of prioritized sweeping in dynamic programming. The original work on PER showed improvements in Atari, but follow-up results were mixed. In this paper, we investigate several variations on PER, to attempt to understand where and when PER may be useful. Our findings in prediction tasks reveal that while PER can improve value propagation in tabular settings, behavior is significantly different when combined with neural networks. Certain mitigations$-$like delaying target network updates to control generalization and using estimates of expected TD errors in PER to avoid chasing stochasticity$-$can avoid large spikes in error with PER and neural networks but generally do not outperform uniform replay. In control tasks, none of the prioritized variants consistently outperform uniform replay. We present new insight into the interaction between prioritization, bootstrapping, and neural networks and propose several improvements for PER in tabular settings and noisy domains.

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RLJ Journal 2024 Journal Article

Investigating the Interplay of Prioritized Replay and Generalization

  • Parham Mohammad Panahi
  • Andrew Patterson
  • Martha White
  • Adam White

Experience replay, the reuse of past data to improve sample efficiency, is ubiquitous in reinforcement learning. Though a variety of smart sampling schemes have been introduced to improve performance, uniform sampling by far remains the most common approach. One exception is Prioritized Experience Replay (PER), where sampling is done proportionally to TD errors, inspired by the success of prioritized sweeping in dynamic programming. The original work on PER showed improvements in Atari, but follow-up results were mixed. In this paper, we investigate several variations on PER, to attempt to understand where and when PER may be useful. Our findings in prediction tasks reveal that while PER can improve value propagation in tabular settings, behavior is significantly different when combined with neural networks. Certain mitigations$-$like delaying target network updates to control generalization and using estimates of expected TD errors in PER to avoid chasing stochasticity$-$can avoid large spikes in error with PER and neural networks but generally do not outperform uniform replay. In control tasks, none of the prioritized variants consistently outperform uniform replay. We present new insight into the interaction between prioritization, bootstrapping, and neural networks and propose several improvements for PER in tabular settings and noisy domains.

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EWRL Workshop 2023 Workshop Paper

A Gradient Critic for Policy Gradient Estimation

  • Samuele Tosatto
  • Andrew Patterson
  • Martha White
  • A. Rupam Mahmood

The policy gradient theorem (Sutton et al. , 2000) prescribes the usage of the on-policy state distribution to approximate the gradient. Most algorithms based on this theorem, in practice, break this assumption introducing a distribution shift that can cause the convergence to poor solutions. In this paper, we propose a new approach of reconstructing the policy gradient from the start state without requiring a particular sampling strategy. The policy gradient calculation in this form can be simplified in terms of a \textsl{gradient critic}, which can be recursively estimated due to a new Bellman equation of gradients. By using temporal-difference updates of the gradient critic from an off-policy data stream, we develop the first estimator that side-steps the distribution shift issue in a model-free way. We prove that, under certain realizability conditions, our estimator is unbiased regardless of the sampling strategy. We empirically show that our technique achieves a superior bias-variance trade-off and performance in the presence of off-policy samples. The extended version of this work can be found in Tosatto et al. (2022), and the implementation of the experiment at github. com/SamuelePolimi/temporal-difference-gradient.

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JMLR Journal 2022 Journal Article

A Generalized Projected Bellman Error for Off-policy Value Estimation in Reinforcement Learning

  • Andrew Patterson
  • Adam White
  • Martha White

Many reinforcement learning algorithms rely on value estimation, however, the most widely used algorithms---namely temporal difference algorithms---can diverge under both off-policy sampling and nonlinear function approximation. Many algorithms have been developed for off-policy value estimation based on the linear mean squared projected Bellman error (MSPBE) and are sound under linear function approximation. Extending these methods to the nonlinear case has been largely unsuccessful. Recently, several methods have been introduced that approximate a different objective---the mean-squared Bellman error (MSBE)---which naturally facilitate nonlinear approximation. In this work, we build on these insights and introduce a new generalized MSPBE that extends the linear MSPBE to the nonlinear setting. We show how this generalized objective unifies previous work and obtain new bounds for the value error of the solutions of the generalized objective. We derive an easy-to-use, but sound, algorithm to minimize the generalized objective, and show that it is more stable across runs, is less sensitive to hyperparameters, and performs favorably across four control domains with neural network function approximation. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2022. ( edit, beta )

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ICML Conference 2022 Conference Paper

A Temporal-Difference Approach to Policy Gradient Estimation

  • Samuele Tosatto
  • Andrew Patterson
  • Martha White
  • A. Rupam Mahmood

The policy gradient theorem (Sutton et al. , 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient. Most algorithms based on this theorem, in practice, break this assumption, introducing a distribution shift that can cause the convergence to poor solutions. In this paper, we propose a new approach of reconstructing the policy gradient from the start state without requiring a particular sampling strategy. The policy gradient calculation in this form can be simplified in terms of a gradient critic, which can be recursively estimated due to a new Bellman equation of gradients. By using temporal-difference updates of the gradient critic from an off-policy data stream, we develop the first estimator that side-steps the distribution shift issue in a model-free way. We prove that, under certain realizability conditions, our estimator is unbiased regardless of the sampling strategy. We empirically show that our technique achieves a superior bias-variance trade-off and performance in presence of off-policy samples.

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JAIR Journal 2021 Journal Article

General Value Function Networks

  • Matthew Schlegel
  • Andrew Jacobsen
  • Zaheer Abbas
  • Andrew Patterson
  • Adam White
  • Martha White

State construction is important for learning in partially observable environments. A general purpose strategy for state construction is to learn the state update using a Recurrent Neural Network (RNN), which updates the internal state using the current internal state and the most recent observation. This internal state provides a summary of the observed sequence, to facilitate accurate predictions and decision-making. At the same time, specifying and training RNNs is notoriously tricky, particularly as the common strategy to approximate gradients back in time, called truncated Back-prop Through Time (BPTT), can be sensitive to the truncation window. Further, domain-expertise—which can usually help constrain the function class and so improve trainability—can be difficult to incorporate into complex recurrent units used within RNNs. In this work, we explore how to use multi-step predictions to constrain the RNN and incorporate prior knowledge. In particular, we revisit the idea of using predictions to construct state and ask: does constraining (parts of) the state to consist of predictions about the future improve RNN trainability? We formulate a novel RNN architecture, called a General Value Function Network (GVFN), where each internal state component corresponds to a prediction about the future represented as a value function. We first provide an objective for optimizing GVFNs, and derive several algorithms to optimize this objective. We then show that GVFNs are more robust to the truncation level, in many cases only requiring one-step gradient updates.

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ICML Conference 2020 Conference Paper

Gradient Temporal-Difference Learning with Regularized Corrections

  • Sina Ghiassian
  • Andrew Patterson
  • Shivam Garg 0006
  • Dhawal Gupta
  • Adam White 0001
  • Martha White

It is still common to use Q-learning and temporal difference (TD) learning{—}even though they have divergence issues and sound Gradient TD alternatives exist{—}because divergence seems rare and they typically perform well. However, recent work with large neural network learning systems reveals that instability is more common than previously thought. Practitioners face a difficult dilemma: choose an easy to use and performant TD method, or a more complex algorithm that is more sound but harder to tune and all but unexplored with non-linear function approximation or control. In this paper, we introduce a new method called TD with Regularized Corrections (TDRC), that attempts to balance ease of use, soundness, and performance. It behaves as well as TD, when TD performs well, but is sound in cases where TD diverges. We empirically investigate TDRC across a range of problems, for both prediction and control, and for both linear and non-linear function approximation, and show, potentially for the first time, that Gradient TD methods could be a better alternative to TD and Q-learning.

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NeurIPS Conference 2019 Conference Paper

Learning Macroscopic Brain Connectomes via Group-Sparse Factorization

  • Farzane Aminmansour
  • Andrew Patterson
  • Lei Le
  • Yisu Peng
  • Daniel Mitchell
  • Franco Pestilli
  • Cesar Caiafa
  • Russell Greiner

Mapping structural brain connectomes for living human brains typically requires expert analysis and rule-based models on diffusion-weighted magnetic resonance imaging. A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals. In this work, we explore a framework that facilitates applying learning algorithms to automatically extract brain connectomes. Using a tensor encoding, we design an objective with a group-regularizer that prefers biologically plausible fascicle structure. We show that the objective is convex and has unique solutions, ensuring identifiable connectomes for an individual. We develop an efficient optimization strategy for this extremely high-dimensional sparse problem, by reducing the number of parameters using a greedy algorithm designed specifically for the problem. We show that this greedy algorithm significantly improves on a standard greedy algorithm, called Orthogonal Matching Pursuit. We conclude with an analysis of the solutions found by our method, showing we can accurately reconstruct the diffusion information while maintaining contiguous fascicles with smooth direction changes.

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IJCAI Conference 2018 Conference Paper

Organizing Experience: a Deeper Look at Replay Mechanisms for Sample-Based Planning in Continuous State Domains

  • Yangchen Pan
  • Muhammad Zaheer
  • Adam White
  • Andrew Patterson
  • Martha White

Model-based strategies for control are critical to obtain sample efficient learning. Dyna is a planning paradigm that naturally interleaves learning and planning, by simulating one-step experience to update the action-value function. This elegant planning strategy has been mostly explored in the tabular setting. The aim of this paper is to revisit sample-based planning, in stochastic and continuous domains with learned models. We first highlight the flexibility afforded by a model over Experience Replay (ER). Replay-based methods can be seen as stochastic planning methods that repeatedly sample from a buffer of recent agent-environment interactions and perform updates to improve data efficiency. We show that a model, as opposed to a replay buffer, is particularly useful for specifying which states to sample from during planning, such as predecessor states that propagate information in reverse from a state more quickly. We introduce a semi-parametric model learning approach, called Reweighted Experience Models (REMs), that makes it simple to sample next states or predecessors. We demonstrate that REM-Dyna exhibits similar advantages over replay-based methods in learning in continuous state problems, and that the performance gap grows when moving to stochastic domains, of increasing size.

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NeurIPS Conference 2018 Conference Paper

Supervised autoencoders: Improving generalization performance with unsupervised regularizers

  • Lei Le
  • Andrew Patterson
  • Martha White

Generalization performance is a central goal in machine learning, particularly when learning representations with large neural networks. A common strategy to improve generalization has been through the use of regularizers, typically as a norm constraining the parameters. Regularizing hidden layers in a neural network architecture, however, is not straightforward. There have been a few effective layer-wise suggestions, but without theoretical guarantees for improved performance. In this work, we theoretically and empirically analyze one such model, called a supervised auto-encoder: a neural network that predicts both inputs (reconstruction error) and targets jointly. We provide a novel generalization result for linear auto-encoders, proving uniform stability based on the inclusion of the reconstruction error---particularly as an improvement on simplistic regularization such as norms or even on more advanced regularizations such as the use of auxiliary tasks. Empirically, we then demonstrate that, across an array of architectures with a different number of hidden units and activation functions, the supervised auto-encoder compared to the corresponding standard neural network never harms performance and can significantly improve generalization.

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