Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

Rémy Portelas (1) , Cédric Colas (1) , Katja Hofmann (2) , Pierre-Yves Oudeyer (1)

1 Flowers - Flowing Epigenetic Robots and Systems
2 Microsoft - Microsoft Research [Cambridge]

We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments. Because it does not initially know the capacities of its student, a key challenge for the teacher is to discover which environments are easy, difficult or unlearnable, and in what order to propose them to maximize the efficiency of learning over the learnable ones. To achieve this, this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student. We present a new algorithm modeling absolute learning progress with Gaussian mixture models (ALP-GMM). We also adapt existing algorithms and provide a complete study in the context of DRL. Using parameterized variants of the BipedalWalker environment, we study their efficiency to personalize a learning curriculum for different learners (embodiments), their robustness to the ratio of learnable/unlearnable environments, and their scalability to non-linear and high-dimensional parameter spaces. Videos and code are available at https://github.com/flowersteam/teachDeepRL.

Mots clés: Curriculum Learning; Learning Progress; Curiosity; Parameterized Procedural Environments; Teacher-Student Learning; Deep Reinforcement Learning

Domaines: Intelligence artificielle [cs.AI]

Rémy Portelas, Cédric Colas, Katja Hofmann, Pierre-Yves Oudeyer. Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments. CoRL 2019 - Conference on Robot Learning, Oct 2019, Osaka, Japan. ⟨hal-02370165⟩