Search Results for author: Philippe Morere

Found 6 papers, 2 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.

Robust Hierarchical Planning with Policy Delegation

no code implementations25 Oct 2020 Tin Lai, Philippe Morere

We propose a novel framework and algorithm for hierarchical planning based on the principle of delegation.

Intrinsic Exploration as Multi-Objective RL

no code implementations6 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.

Continuous Control

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).


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

Learning to Plan Hierarchically from Curriculum

1 code implementation18 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.

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