Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience

no code implementations • 3 Apr 2024 • Manfred Diaz, Liam Paull, Andrea Tacchetti

Teacher-Student Curriculum Learning (TSCL) is a curriculum learning framework that draws inspiration from human cultural transmission and learning.

Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization

no code implementations • 10 Oct 2021 • Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman, Hiroki Furuta, Seyed Kamyar Seyed Ghasemipour, Anton Raichuk, Byron David, Erik Frey, Erwin Coumans, Olivier Bachem

While reward maximization is at the core of RL, reward engineering is not the only -- sometimes nor the easiest -- way for specifying complex behaviors.

Continuous Control Efficient Exploration +1

LOCO: Adaptive exploration in reinforcement learning via local estimation of contraction coefficients

no code implementations • ICLR Workshop SSL-RL 2021 • Manfred Diaz, Liam Paull, Pablo Samuel Castro

We offer a novel approach to balance exploration and exploitation in reinforcement learning (RL).

reinforcement-learning Reinforcement Learning (RL)

Active Domain Randomization

2 code implementations • 9 Apr 2019 • Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher J. Pal, Liam Paull

Our experiments show that domain randomization may lead to suboptimal, high-variance policies, which we attribute to the uniform sampling of environment parameters.

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