Exchangeable Models in Meta Reinforcement Learning
One recent approach to meta reinforcement learning (meta-RL) is to integrate models for task inference with models for control. The former component is often based on recurrent neural networks, which do not directly exploit the exchangeable structure of the inputs. We propose to use a lightweight, yet an expressive architecture that accounts for exchangeability. Combined with an off-policy reinforcement learning algorithm, it results in a meta-RL method that is sample-efficient, fast to train and able to quickly adapt to new test tasks as demonstrated on a couple of widely used benchmarks.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
