TOM ZAHAVY
Deep Reinforcement Learning
tomzahavy (at) gmail (dot) com
I am a staff research scientist at Google DeepMind, in the discovery team, led by Satinder Singh. Before that I was a Ph.D. student at the Technion working with Shie Mannor. I also interned at Microsoft, Walmart, Facebook, and Google where I worked with Yishay Mansour. I am an experimentalist at heart, but I enjoy theory from time to time, particularly in optimization. I also care a lot about physics.
I am interested in building artificial intelligence systems that make decisions and learn from them. I have been mainly focusing on the Reinforcement Learning paradigm for this. In my PhD I studied aspects of scalability, structure discovery, hierarchy, abstraction, and exploration in DeepRL. I was also one of the first people to work on RL for Minecraft and Language games. My research over the last few years focused on two sub topics of RL: learning to RL learn (meta RL) and unsupervised RL. My papers on these topics can be found below.
For more accessible intro to my work, you can read articles on my work in Quanta, the New Scientist or listen to the Machine Learning Street Talk podcast.
I am currently studying creative problem-solving mechanisms in computationally bounded systems. My recent paper focuses on chess and brings ideas from quality-diversity to AlphaZero. We show how to train a diverse team of chess players that play chess differently, solve more puzzles together, outperform a more homogeneous team, and specialize in different openings. Read our preprint here!
Personal life: I come from a small town in 🇮🇱 on the Mediterranean Sea. I am currently living in London 🇬🇧 and I spent some time in the 🇺🇸. My family is coming from 🇩🇪🇮🇩🇱🇺 and by DNA I am 🇮🇩🇮🇹🇮🇷(50/30/20). I am married to Gili, a singer-songwriter from 🇮🇩🇲🇦🇮🇱. I love spending my free time outdoors in camping, hiking, 4X4 driving, mountaineering, skiing, and scuba diving. When I am at home, my hobbies are running, basketball, and reading science-fiction.
General objectives for RL
We can describe the standard RL problem as a linear function, the inner product between the state-action occupancy and the reward vector. The occupancy is a distribution over the states and actions that an agent visits when following a policy and the reward defines a priority over these state-action pairs.
Sometimes, a good objective function is all we need; predicting the next word in a sentence turned out to be transformative. I've been interested in deriving more general objectives of behaviour: non linear, unsupervised, convex and non convex objectives that control the distribution that an agent visits. These include:
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Maximum entropy exploration — visit all the states equally
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Apprenticeship Learning — visit similar states to another policy
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Diversity — visit states other policies do not
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Constraints
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And more
My main result is that we can reformulate these problems as convex-concave zero sum games, and derive a non stationary intrinsic reward for solving them. The reward turns out to be very simple and general; it is the gradient of the objective w.r.t to the state occupancy.
My papers below study the following questions:
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In what sense are general utility RL problems different from RL problems?
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Can we solve them with similar techniques?
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Are there interesting objectives we can now easily solve using this approach?
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In particular, I worked on Quality-Diversity objectives
Reward is enough for convex MDPs, NeurIPS 2021 (spotlight)
Tl;dr: we study non linear and unsupervised objectives that are defined over the state occupancy of an RL agent in an MDP. These include Apprenticeship Learning, diverse skill discovery, constrained MDPs and pure exploration. We show that maximizing the gradient of such an objective, as an intrinsic reward, solves the problem efficiently. We also propose a meta algorithm and show that many existing algorithms in the literature can be explained as instances of it.
Tom Zahavy, Brendan O'Donoghue, Guillaume Desjardins, Satinder Singh
Discovering Policies with DOMiNO: Diversity Optimization Maintaining Near Optimality, ICLR 2023
Tl;dr We propose intrinsic rewards for discovering quality-diverse policies and show that they adapt to changes in the environment.
Discovering a set of policies for the worst case reward, ICLR 2021 (spotlight)
Tl;dr We propose a method for discovering a set of policies that perform well w.r.t the worst case reward when composed together.
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Tl;dr We propose a practical DRL algoritthm that converges (last iterate) in constrained MDPs and show empirically that it reduces oscilations in the DM control suite.
Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah, Sebastian Flennerhag, Satinder Singh, Tom Zahavy
Discovering Diverse Nearly Optimal Policies with Successor Features
Tl;dr We propose a method for discovering policies that are diverse in the space of Successor Features, while assuring that they are near optimal using a constrained MDP.
Tom Zahavy, Brendan O'Donoghue, Andre Barreto, Volodymyr Mnih, Sebastian Flennerhag, Satinder Singh
Apprenticeship Learning via Frank-Wolfe, AAAI 2020
Tl;dr We show that the well-known Apprenticeship Learning algorithm of Abbeel and Ng (2004) can be understood as a Frank-Wolfe method and propose methods to accelerate it.
Tom Zahavy, Alon Cohen, Haim Kaplan, and Yishay Mansour
Online Apprenticeship Learning, AAAI 2021
Tl;dr We propose the first Apprenticeship Learning algorithm that does not require to solve an MDP in each iteration and analyse its regret.
Lior Shani, Tom Zahavy, Shie Mannor
Meta RL
Building algorithms that learn how to learn and get better at doing so over time. I've worked mostly on meta-gradients, ie, using gradients to learn the meta parameters (hyper parameters, loss function, options, reward) of RL agents, so they become better and better at solving the original problem.
Read more about meta-gradients in my papers below, and please check out my interview with Robert, Tim, and Yanick in the MLST Podcast. I also recommend this excellent blog post by Robert Langer, and this talk by David Silver, featuring many meta-gradient papers including my own.
A Self-Tuning Actor-Critic Algorithm, NeurIPS 2020
Tl;dr: We propose a self-tuning actor-critic algorithm (STACX) that adapts all the differentiable hyper parameters of IMPALA including those of auxiliary tasks and achieves impressive gains in performance in Atari and DM control.
Tom Zahavy, Zhongwen Xu, Vivek Veeriah, Matteo Hessel, Junhyuk Oh, Hado van Hasselt, David Silver, Satinder Singh
Bootstrapped Meta Learning, ICLR 2022 (outstanding paper award)
Tl;dr: We propose a novel meta learning algorithm that first bootstraps a target from the meta-learner, then optimises the meta-learner by minimising the distance to that target. When applied to STACX it achieves SOTA results in Atari.
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh
Discovery of Options via Meta-Learned Subgoals, NeurIPS 2021
Tl;dr: we use meta gradients to discover subgoals, in the form of intrinsic rewards, uses these subgoals to learn options, and control these options with an HRL policy.
Vivek Veeriah, Tom Zahavy, Matteo Hessel, Zhongwen Xu, Junhyuk Oh, Iurii Kemaev, Hado van Hasselt, David Silver, Satinder Singh
POMRL: No-Regret Learning-to-Plan with Increasing Horizons, TMLR, 2023
Tl;dr: We study how meta RL can exploit structure in the meta task distribution to guarantee faster RL.
Khimya Khetarpal, Claire Vernade, Brendan O'Donoghue, Satinder Singh, Tom Zahavy
Balancing Constraints and Rewards with Meta-Gradient D4PG, ICLR 2021
Tl;dr: We use meta gradients to adapt the learning rates of the RL agent and the Lagrange multiplier in a constrained MDP.
Meta Gradients in Non Stationary Environments, CoLLAs 2022 (Oral)
Tl;dr: We study meta gradients in non stationary RL environments.
Jelena Luketina, Sebastian Flennerhag, Yannick Schroecker, David Abel, Tom Zahavy Satinder Singh
Optimistic Meta-Gradients
Tl;dr: We study the convergence of meta gradients (mg) and show that bootstrapped mg give acceleration.
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue, Hado van Hasselt, András György, Satinder Singh
Discovering Evolution Strategies via Meta-Black-Box Optimization, ICLR 2023
Tl;dr: We use evolutionary strategies to meta learn how to learn with evolutionary strategies.
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy, Valenti Dallibard, Chris Lu, Satinder Singh, Sebastian Flennerhag
Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization,
GECCO 2023 (nominated for best paper award)
Tl;dr: We use evolutionary strategies to meta learn how to learn with genetic algorithms.
Robert Tjarko Lange, Tom Schaul, Yutian Chen, Chris Lu, Tom Zahavy, Valentin Dallibard, Sebastian Flennerhag
Older Publications from my PhD
A Deep Hierarchical Approach to Lifelong Learning in Minecraft
AAAI 2017
Chen Tessler, Shahar Givony, Tom Zahavy, Daniel J Mankowitz, Shie Mannor
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
NeurIPS 2018
Tom Zahavy, Matan Haroush, Nadav Merlis, Daniel J. Mankowitz, Shie Mannor
Graying the black box: Understanding DQNs
ICML 2016
Tom Zahavy, Nir Ben Zrihem, Shie Mannor
Online Limited Memory Neural-Linear Bandits with Likelihood Matching
ICML 2021
Inverse Reinforcement Learning in Contextual MDPs
SPRINGER, Machine Learning Journal 2021, Special Issue On RL for Real Life
Stav Belogolovsky, Philip Korsunsky, Shie Mannor, Chen Tessler, Tom Zahavy
Shallow Updates for Deep Reinforcement Learning
NeurIPS 2017
Planning in Hierarchical Reinforcement Learning: Guarantees for Using Local Policies
ALT 2020
Tom Zahavy, Avinatan Hasidim, Haim Kaplan and Yishay Mansour