Search Results for author: Gilad Francis

Found 5 papers, 1 papers with code

Learning from Demonstration without Demonstrations

1 code implementation17 Jun 2021 Tom Blau, Gilad Francis, Philippe Morere

To address this shortcoming, we propose Probabilistic Planning for Demonstration Discovery (P2D2), a technique for automatically discovering demonstrations without access to an expert.

Structural clustering of volatility regimes for dynamic trading strategies

no code implementations21 Apr 2020 Arjun Prakash, Nick James, Max Menzies, Gilad Francis

We develop a new method to find the number of volatility regimes in a nonstationary financial time series by applying unsupervised learning to its volatility structure.

Change Point Detection Time Series

Reinforcement Learning with Probabilistically Complete Exploration

no code implementations20 Jan 2020 Philippe Morere, Gilad Francis, Tom Blau, Fabio Ramos

Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL).

reinforcement-learning

OCTNet: Trajectory Generation in New Environments from Past Experiences

no code implementations25 Sep 2019 Weiming Zhi, Tin Lai, Lionel Ott, Gilad Francis, Fabio Ramos

This generally involves the prediction and understanding of motion patterns of dynamic entities, such as vehicles and people, in the surroundings.

motion prediction

Bayesian Local Sampling-based Planning

no code implementations8 Sep 2019 Tin Lai, Philippe Morere, Fabio Ramos, Gilad Francis

In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution.

Motion Planning

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