Meta-Reinforcement Learning in Broad and Non-Parametric Environments
- URL: http://arxiv.org/abs/2108.03718v1
- Date: Sun, 8 Aug 2021 19:32:44 GMT
- Title: Meta-Reinforcement Learning in Broad and Non-Parametric Environments
- Authors: Zhenshan Bing, Lukas Knak, Fabrice Oliver Robin, Kai Huang, Alois
Knoll
- Abstract summary: We introduce TIGR, a Task-Inference-based meta-RL algorithm for tasks in non-parametric environments.
We decouple the policy training from the task-inference learning and efficiently train the inference mechanism on the basis of an unsupervised reconstruction objective.
We provide a benchmark with qualitatively distinct tasks based on the half-cheetah environment and demonstrate the superior performance of TIGR compared to state-of-the-art meta-RL approaches.
- Score: 8.091658684517103
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Recent state-of-the-art artificial agents lack the ability to adapt rapidly
to new tasks, as they are trained exclusively for specific objectives and
require massive amounts of interaction to learn new skills. Meta-reinforcement
learning (meta-RL) addresses this challenge by leveraging knowledge learned
from training tasks to perform well in previously unseen tasks. However,
current meta-RL approaches limit themselves to narrow parametric task
distributions, ignoring qualitative differences between tasks that occur in the
real world. In this paper, we introduce TIGR, a Task-Inference-based meta-RL
algorithm using Gaussian mixture models (GMM) and gated Recurrent units,
designed for tasks in non-parametric environments. We employ a generative model
involving a GMM to capture the multi-modality of the tasks. We decouple the
policy training from the task-inference learning and efficiently train the
inference mechanism on the basis of an unsupervised reconstruction objective.
We provide a benchmark with qualitatively distinct tasks based on the
half-cheetah environment and demonstrate the superior performance of TIGR
compared to state-of-the-art meta-RL approaches in terms of sample efficiency
(3-10 times faster), asymptotic performance, and applicability in
non-parametric environments with zero-shot adaptation.
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