1 code implementation • 18 Jun 2019 • Philippe Morere, Lionel Ott, Fabio Ramos
Our framework decomposes transition dynamics into skill effects and success conditions, which allows fast planning by reasoning on effects, while learning conditions from interactions with the world.
no code implementations • 8 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.
no code implementations • 20 Jan 2020 • Philippe Morere, Gilad Francis, Tom Blau, Fabio Ramos
Balancing exploration and exploitation remains a key challenge in reinforcement learning (RL).
no code implementations • 6 Apr 2020 • Philippe Morere, Fabio Ramos
To overcome this problem, we propose a framework based on multi-objective RL where both exploration and exploitation are being optimized as separate objectives.
no code implementations • 25 Oct 2020 • Tin Lai, Philippe Morere
We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation.
1 code implementation • 17 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.