… arises with meta-learning exploration is: if our goal is to learn … diverse and challenging tasks
at meta-training time to acquire … reward tasks to meta-learn exploration strategies that work …

… meta reinforcement learning approach that achieves online adaptation in dynamic environments.
To the best knowledge of the authors, this is the first meta-reinforcement learning … meta …

… In order to study the capabilities of current multi-task and meta-reinforcement learning … We
contend that multi-task and meta reinforcement learning methods that aim to efficiently learn …

… meta-training is a simple and cheap way to boost the performance of meta-RL agents; 2) We
show that we can train such meta-… that our agents can solve difficult meta-RL problems in …

… meta-RL: few-shot meta-RL. Here, the goal is to learn an RL algorithm capable of fast adaptation,
ie, learning … on a given task distribution, and meta-learn how to efficiently adapt to any …

… Although we cannot rule out the possibility that forms of meta-learning may also emerge in
these loops, we observe that the meta-RL effect described in this paper emerges only when …

… tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have
emerged as a popular solution to achieve this. However, metareinforcement learning (meta-RL) …

… of learning. We derive a practical gradient-based meta-learning algorithm and show that this
… significantly improve performance on large-scale deep reinforcement learning applications. …

… Meta-parameters in reinforcement learning should be tuned … meta-reinforcement learning
algorithm for tuning these meta-… appropriate meta-parameter values, and controls the meta-…

… This paper introduces the offline metareinforcement learning (offline meta-RL) problem …
Offline meta-RL is analogous to the widely successful supervised learning strategy of pre-training …