Search Results for author: Samuel Sokota

Found 9 papers, 2 papers with code

Learning to Coordinate with Humans using Action Features

no code implementations29 Jan 2022 Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Foerster

An unaddressed challenge in human-AI coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations.

Monte Carlo Tree Search With Iteratively Refining State Abstractions

no code implementations NeurIPS 2021 Samuel Sokota, Caleb Ho, Zaheen Ahmad, J. Zico Kolter

In this work, we present a method, called abstraction refining, for extending MCTS to stochastic environments which, unlike progressive widening, leverages the geometry of the state space.

A Fine-Tuning Approach to Belief State Modeling

no code implementations ICLR 2022 Samuel Sokota, Hengyuan Hu, David J Wu, J Zico Kolter, Jakob Nicolaus Foerster, Noam Brown

Furthermore, because this specialization occurs after the action or policy has already been decided, BFT does not require the belief model to process it as input.

Communicating via Markov Decision Processes

no code implementations29 Sep 2021 Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa M Zintgraf, Philip Torr, J Zico Kolter, Shimon Whiteson, Jakob Nicolaus Foerster

We consider the problem of communicating exogenous information by means of Markov decision process trajectories.

Zero-Shot Coordination via Semantic Relationships Between Actions and Observations

no code implementations29 Sep 2021 Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster

An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations.

Implicit Communication as Minimum Entropy Coupling

no code implementations17 Jul 2021 Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa Zintgraf, Philip Torr, Shimon Whiteson, Jakob Foerster

In many common-payoff games, achieving good performance requires players to develop protocols for communicating their private information implicitly -- i. e., using actions that have non-communicative effects on the environment.

Multi-agent Reinforcement Learning

Solving Common-Payoff Games with Approximate Policy Iteration

2 code implementations11 Jan 2021 Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot

While this choice precludes CAPI from scaling to games as large as Hanabi, empirical results demonstrate that, on the games to which CAPI does scale, it is capable of discovering optimal joint policies even when other modern multi-agent reinforcement learning algorithms are unable to do so.

Multi-agent Reinforcement Learning reinforcement-learning

Selective Dyna-style Planning Under Limited Model Capacity

no code implementations ICML 2020 Zaheer Abbas, Samuel Sokota, Erin J. Talvitie, Martha White

We show that heteroscedastic regression can signal predictive uncertainty arising from model inadequacy that is complementary to that which is detected by methods designed for parameter uncertainty, indicating that considering both parameter uncertainty and model inadequacy may be a more promising direction for effective selective planning than either in isolation.

Model-based Reinforcement Learning

Simultaneous Prediction Intervals for Patient-Specific Survival Curves

1 code implementation25 Jun 2019 Samuel Sokota, Ryan D'Orazio, Khurram Javed, Humza Haider, Russell Greiner

In this paper, we demonstrate that an existing method for estimating simultaneous prediction intervals from samples can easily be adapted for patient-specific survival curve analysis and yields accurate results.

Prediction Intervals Survival Analysis

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