# Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling

arXiv: https://arxiv.org/abs/2203.16801 · HTML: https://mlsanity.com/paper/2203.16801/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://mlsanity.com/paper/1903.08254/) (2019)
- [Agent57: Outperforming the Atari Human Benchmark](https://mlsanity.com/paper/2003.13350/) (2020)
- [Model-based Adversarial Meta-Reinforcement Learning](https://mlsanity.com/paper/2006.08875/) (2020)
- [3D Simulation for Robot Arm Control with Deep Q-Learning](https://mlsanity.com/paper/1609.03759/) (2016)
- [Curriculum in Gradient-Based Meta-Reinforcement Learning](https://mlsanity.com/paper/2002.07956/) (2020)

## Built on by (most cited first, 1 of 1)

- [L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning](https://mlsanity.com/paper/2305.13741/) (2023)

## Abstract (arXiv)

Meta-reinforcement learning (meta-RL) acquires meta-policies that show good performance for tasks in a wide task distribution. However, conventional meta-RL, which learns meta-policies by randomly sampling tasks, has been reported to show meta-overfitting for certain tasks, especially for easy tasks where an agent can easily get high scores. To reduce effects of the meta-overfitting, we considered meta-RL with curriculum-based task sampling. Our method is Robust Meta Reinforcement Learning with Guided Task Sampling (RMRL-GTS), which is an effective method that restricts task sampling based on scores and epochs. We show that in order to achieve robust meta-RL, it is necessary not only to intensively sample tasks with poor scores, but also to restrict and expand the task regions of the tasks to be sampled.