Khimya Khetarpal

Clare Lyle

Razvan Pascanu

Georgios Piliouras

Jonathan Richens

Mark Rowland

Tom Schaul

Satinder Singh

Agency is a system's capacity to steer outcomes toward a goal, and is a central topic of study across biology, philosophy, cognitive science… (voir plus), and artificial intelligence. Determining if a system exhibits agency is a notoriously difficult question: Dennett (1989), for instance, highlights the puzzle of determining which principles can decide whether a rock, a thermostat, or a robot each possess agency. We here address this puzzle from the viewpoint of reinforcement learning by arguing that agency is fundamentally frame-dependent: Any measurement of a system's agency must be made relative to a reference frame. We support this claim by presenting a philosophical argument that each of the essential properties of agency proposed by Barandiaran et al. (2009) and Moreno (2018) are themselves frame-dependent. We conclude that any basic science of agency requires frame-dependence, and discuss the implications of this claim for reinforcement learning.

2025-02-05

ArXiv (prépublication)

Optimizing Return Distributions with Distributional Dynamic Programming

Bernardo Avila Pires

Mark Rowland

Diana Borsa

Zhaohan Daniel Guo

Andre Barreto

David Abel

Remi Munos

Will Dabney

We introduce distributional dynamic programming (DP) methods for optimizing statistical functionals of the return distribution, with standar… (voir plus)d reinforcement learning as a special case. Previous distributional DP methods could optimize the same class of expected utilities as classic DP. To go beyond expected utilities, we combine distributional DP with stock augmentation, a technique previously introduced for classic DP in the context of risk-sensitive RL, where the MDP state is augmented with a statistic of the rewards obtained so far (since the first time step). We find that a number of recently studied problems can be formulated as stock-augmented return distribution optimization, and we show that we can use distributional DP to solve them. We analyze distributional value and policy iteration, with bounds and a study of what objectives these distributional DP methods can or cannot optimize. We describe a number of applications outlining how to use distributional DP to solve different stock-augmented return distribution optimization problems, for example maximizing conditional value-at-risk, and homeostatic regulation. To highlight the practical potential of stock-augmented return distribution optimization and distributional DP, we combine the core ideas of distributional value iteration with the deep RL agent DQN, and empirically evaluate it for solving instances of the applications discussed.

2025-01-21

ArXiv (prépublication)

Balancing Context Length and Mixing Times for Reinforcement Learning at Scale

Matthew Riemer

Janarthanan Rajendran

Sarath Chandar

Due to the recent remarkable advances in artiﬁcial intelligence, researchers have begun to consider challenging learning problems such as … (voir plus)learning to generalize behavior from large ofﬂine datasets or learning online in non-Markovian environments. Meanwhile, recent advances in both of these areas have increasingly relied on conditioning policies on large context lengths. A natural question is if there is a limit to the performance beneﬁts of increasing the context length if the computation needed is available. In this work, we establish a novel theoretical result that links the context length of a policy to the time needed to reliably evaluate its performance (i.e., its mixing time) in large scale partially observable reinforcement learning environments that exhibit latent sub-task structure. This analysis underscores a key tradeoff: when we extend the context length, our policy can more effectively model non-Markovian dependencies, but this comes at the cost of potentially slower policy evaluation and as a result slower downstream learning. Moreover, our empirical results highlight the relevance of this analysis when leveraging Transformer based neural networks. This perspective will become increasingly pertinent as the ﬁeld scales towards larger and more realistic environments, opening up a number of potential future directions for improving the way we design learning agents.

2024-12-10

Neural Information Processing Systems (Accept (poster))

Normalization and effective learning rates in reinforcement learning

Clare Lyle

Zeyu Zheng

James Martens

Hado van Hasselt

Razvan Pascanu

Will Dabney

2024-12-09

Advances in Neural Information Processing Systems 37 (publié)

Toward Human-AI Alignment in Large-Scale Multi-Player Games

Sugandha Sharma

Guy Davidson

Anssi Kanervisto

Udit Arora

Katja Hofmann

Ida Momennejad

Achieving human-AI alignment in complex multi-agent games is crucial for creating trustworthy AI agents that enhance gameplay. We propose a … (voir plus)method to evaluate this alignment using an interpretable task-sets framework, focusing on high-level behavioral tasks instead of low-level policies. Our approach has three components. First, we analyze extensive human gameplay data from Xbox's Bleeding Edge (100K+ games), uncovering behavioral patterns in a complex task space. This task space serves as a basis set for a behavior manifold capturing interpretable axes: fight-flight, explore-exploit, and solo-multi-agent. Second, we train an AI agent to play Bleeding Edge using a Generative Pretrained Causal Transformer and measure its behavior. Third, we project human and AI gameplay to the proposed behavior manifold to compare and contrast. This allows us to interpret differences in policy as higher-level behavioral concepts, e.g., we find that while human players exhibit variability in fight-flight and explore-exploit behavior, AI players tend towards uniformity. Furthermore, AI agents predominantly engage in solo play, while humans often engage in cooperative and competitive multi-agent patterns. These stark differences underscore the need for interpretable evaluation, design, and integration of AI in human-aligned applications. Our study advances the alignment discussion in AI and especially generative AI research, offering a measurable framework for interpretable human-agent alignment in multiplayer gaming.

2024-02-04

ArXiv (prépublication)

Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels

The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and th… (voir plus)us enables sample-efficient learning. This becomes particularly beneficial in complex environments from high-dimensional state space such as pixels, where the goal is distant and the reward sparse. We introduce Forecaster, a deep hierarchical reinforcement learning approach which plans over high-level goals leveraging a temporally abstract world model. Forecaster learns an abstract model of its environment by modelling the transitions dynamics at an abstract level and training a world model on such transition. It then uses this world model to choose optimal high-level goals through a tree-search planning procedure. It additionally trains a low-level policy that learns to reach those goals. Our method not only captures building world models with longer horizons, but also, planning with such models in downstream tasks. We empirically demonstrate Forecaster's potential in both single-task learning and generalization to new tasks in the AntMaze domain.

2023-10-27

GenPlan @ Neural Information Processing Systems (publié)

Discovering Object-Centric Generalized Value Functions From Pixels

Somjit Nath

Gopeshh Raaj Subbaraj

Samira Ebrahimi Kahou

Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using ha… (voir plus)nd-crafted auxiliary tasks and pseudo rewards. Automatically learning such representations in an object-centric manner geared towards control and fast adaptation remains an open research problem. In this paper, we introduce a method that tries to discover meaningful features from objects, translating them to temporally coherent "question" functions and leveraging the subsequent learned general value functions for control. We compare our approach with state-of-the-art techniques alongside other ablations and show competitive performance in both stationary and non-stationary settings. Finally, we also investigate the discovered general value functions and through qualitative analysis show that the learned representations are not only interpretable but also, centered around objects that are invariant to changes across tasks facilitating fast adaptation.

2023-07-24

International Conference on Machine Learning (Accept (Poster))

proceedings.mlr.press

POMRL: No-Regret Learning-to-Plan with Increasing Horizons

Claire Vernade

Brendan O'Donoghue

Satinder Singh

Tom Zahavy

We study the problem of planning under model uncertainty in an online meta-reinforcement learning (RL) setting where an agent is presented w… (voir plus)ith a sequence of related tasks with limited interactions per task. The agent can use its experience in each task and across tasks to estimate both the transition model and the distribution over tasks. We propose an algorithm to meta-learn the underlying structure across tasks, utilize it to plan in each task, and upper-bound the regret of the planning loss. Our bound suggests that the average regret over tasks decreases as the number of tasks increases and as the tasks are more similar. In the classical single-task setting, it is known that the planning horizon should depend on the estimated model's accuracy, that is, on the number of samples within task. We generalize this finding to meta-RL and study this dependence of planning horizons on the number of tasks. Based on our theoretical findings, we derive heuristics for selecting slowly increasing discount factors, and we validate its significance empirically.

2023-07-17

TMLR (accepté)

Towards Continual Reinforcement Learning: A Review and Perspectives

Matthew D Riemer

Irina Rish

2022-12-21

Journal of Artificial Intelligence Research (publié)

The Paradox of Choice: On the Role of Attention in Hierarchical Reinforcement Learning

Andrei Cristian Nica

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to … (voir plus)having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth, it is desirable to restrict the agent's attention at each step to a reasonable number of possible choices. The concept of affordances (Gibson, 1977) suggests that only certain actions are feasible in certain states. In this work, we first characterize "affordances" as a "hard" attention mechanism that strictly limits the available choices of temporally extended options. We then investigate the role of hard versus soft attention in training data collection, abstract value learning in long-horizon tasks, and handling a growing number of choices. To this end, we present an online, model-free algorithm to learn affordances that can be used to further learn subgoal options. Finally, we identify and empirically demonstrate the settings in which the "paradox of choice" arises, i.e. when having fewer but more meaningful choices improves the learning speed and performance of a reinforcement learning agent.

2022-10-19

NeurIPS.cc/2022/Workshop/Attention (poster)

The Paradox of Choice: Using Attention in Hierarchical Reinforcement Learning

Andrei Cristian Nica

2022-01-23

ArXiv (prépublication)

Temporally Abstract Partial Models

Zafarali Ahmed

Gheorghe Comanici

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement l… (voir plus)earning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we demonstrate empirically the potential impact of partial option models on the efficiency of planning.

2021-11-08

NeurIPS.cc/2021/Conference (poster)