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Gilad Katz

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5 papers

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AAMAS Conference 2026 Conference Paper

Reducing Overestimation by Measuring Critic Disagreement in Multi-Critics Architectures

Nitsan Soffair
Gilad Katz

We introduce Ensemble Std (ES), a lightweight regularizer for multicritic deep reinforcement learning that mitigates overestimation by calibrating target values according to critic disagreement. ES treats the dispersion of target-value estimates across an ensemble of Q-functions as an uncertainty signal, applying an adaptive subtractive penalty when disagreement is high. This yields uncertainty-aware, conservative targets that preserve the learning signal where critics agree and temper optimism where they do not–complementing minimum-based targets without imposing uniformly pessimistic updates. ES operates directly on critic targets, requiring no architectural changes, and integrates seamlessly into a wide range of actor–critic algorithms. In practice, ES was easily plugged into TD3, SAC, and TD3+BC with negligible overhead, consistently improving stability and returns while reducing variance. Overall, ES offers a simple, conceptually transparent mechanism that turns ensemble disagreement into principled value regularization, making multi-critic learners more robust in noisy, uncertain, and data-limited regimes. Our code is available at https: //github. com/anonymouszxcv16/ES.

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AAMAS Conference 2026 Conference Paper

Team of Rivals: Hierarchical Deep Reinforcement Learning and Behavior Cloning for Multiplayer Poker

Avishag Shapira
Ido Rom
Asaf Shabtai
Gilad Katz

Multiplayer no-limit Texas Hold’em is considered a challenging benchmark for AI algorithms, due to the need for decision making under partial information, strategic deception, and non-stationary opponents. Classical equilibrium-based techniques do not extend cleanly to the multiplayer setting, and prevailing multiplayer solutions, such as LLMs, tend to be computationally intensive. This study introduces Havoc, a hierarchical deep RL approach that combines behavior cloning of individual human experts with a value-basedmasterpolicythatselects, ateachdecisionpoint, which specialist to deploy. By preserving distinct human play styles in the specialist policies and learning when to deploy them, Havoc adapts itsstrategyrapidlyastableconditionsshift. Despitelimitedtraining data, Havoc attains strong multiplayer performance, outperforming current state-of-the-art methods while also requiring substantially less computational resources.

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TMLR Journal 2025 Journal Article

Generative Proto-Sequence: Sequence-Level Decision Making for Long-Horizon Reinforcement Learning

Netanel Fried
Liad Giladi
Gilad Katz

Deep reinforcement learning (DRL) methods often face challenges in environments characterized by large state spaces, long action horizons, and sparse rewards, where effective exploration and credit assignment are critical. We introduce Generative Proto-Sequence (GPS), a novel generative DRL approach that produces variable-length discrete action sequences. By generating entire action sequences in a single decision rather than selecting individual actions at each timestep, GPS reduces the temporal decision bottleneck that impedes learning in long-horizon tasks. This sequence-level abstraction provides three key advantages: (1) it facilitates more effective credit assignment by directly connecting state observations with the outcomes of complete behavioral patterns; (2) by committing to coherent multi-step strategies, our approach facilitates better exploration of the state space; and (3) it promotes better generalization by learning macro-behaviors that transfer across similar situations rather than memorizing state-specific responses. Evaluations across diverse maze navigation tasks of varying sizes and complexities demonstrate that GPS outperforms leading action repetition and temporal methods in the large majority of tested configurations, where it converges faster and achieves higher success rates.

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AAAI Conference 2022 Conference Paper

Q-Ball: Modeling Basketball Games Using Deep Reinforcement Learning

Chen Yanai
Adir Solomon
Gilad Katz
Bracha Shapira
Lior Rokach

Basketball is one of the most popular types of sports in the world. Recent technological developments have made it possible to collect large amounts of data on the game, analyze it, and discover new insights. We propose a novel approach for modeling basketball games using deep reinforcement learning. By analyzing multiple aspects of both the players and the game, we are able to model the latent connections among players’ movements, actions, and performance, into a single measure – the Q-Ball. Using Q-Ball, we are able to assign scores to the performance of both players and whole teams. Our approach has multiple practical applications, including evaluating and improving players’ game decisions and producing tactical recommendations. We train and evaluate our approach on a large dataset of National Basketball Association games, and show that the Q-Ball is capable of accurately assessing the performance of players and teams. Furthermore, we show that Q-Ball is highly effective in recommending alternatives to players’ actions.

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TIST Journal 2017 Journal Article

Vertical Ensemble Co-Training for Text Classification

Gilad Katz
Cornelia Caragea
Asaf Shabtai

High-quality, labeled data is essential for successfully applying machine learning methods to real-world text classification problems. However, in many cases, the amount of labeled data is very small compared to that of the unlabeled, and labeling additional samples could be expensive and time consuming. Co-training algorithms, which make use of unlabeled data to improve classification, have proven to be very effective in such cases. Generally, co-training algorithms work by using two classifiers, trained on two different views of the data, to label large amounts of unlabeled data. Doing so can help minimize the human effort required for labeling new data, as well as improve classification performance. In this article, we propose an ensemble-based co-training approach that uses an ensemble of classifiers from different training iterations to improve labeling accuracy. This approach, which we call vertical ensemble, incurs almost no additional computational cost. Experiments conducted on six textual datasets show a significant improvement of over 45% in AUC compared with the original co-training algorithm.

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