<p>Meta-Reinforcement Learning can refer to a broad range of ideas. Also, different algorithms are SOTA under different evaluation metrics (sample efficiency, agent performance, adaptation speed on a new task, etc)</p>
<p>Assuming that you are referring to the problem of quickly learning/adapting to a new task by training an agent on a distribution of related tasks, the following are some popular algorithms</p>
<ul>
<li>PEARL [Rakelly et al., 2019]</li>
<li>VariBAD [Zintgraf et al., 2020]</li>
<li>Meta-Q-Learning [Fakoor et al., 2020]</li>
</ul>
<p><strong>References:</strong></p>
<ol>
<li>K Rakelly, A Xhou, D Quillen, C Finn, S Levine - <a href="https://arxiv.org/abs/1903.08254" rel="nofollow noreferrer">Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables</a>, ICML 2019.</li>
<li>L Zintgraf et al., - <a href="https://arxiv.org/abs/1910.08348" rel="nofollow noreferrer">VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning</a>, ICLR 2020.</li>
<li>R Fakoor, P Chaudhari, S Soatto, A J Smola - <a href="https://arxiv.org/abs/1910.00125" rel="nofollow noreferrer">Meta-Q-Learning</a>, ICLR 2020.</li>
</ol>

CC BY-SA 4.0
2021-02-22T20:30:52.260Z
2021-02-22T20:30:52.260Z
Mohith Kaameswaran Sakthivel
<p>One of the most recent papers on meta-RL is <a href="https://arxiv.org/pdf/1910.00125.pdf" rel="nofollow noreferrer">meta-Q-learning</a>. This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-reinforcement learning (meta-RL). MQL builds upon three simple ideas.</p>
<ul>
<li><p>Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory.</p>
</li>
<li><p>Using a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies.</p>
</li>
<li><p>past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates</p>
</li>
</ul>
<p>Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with state-of-the-art meta-RL algorithms.</p>
<p>I think that other references to other work on meta-RL are present in the experiments part of the MQL paper.</p>

CC BY-SA 4.0
2020-02-08T11:30:22.463Z
2021-02-23T10:19:43.790Z
2021-02-23T10:19:43.790Z
hola
<p>This question can seem a little bit too broad, but I am wondering what are the current state-of-the-art works on meta reinforcement learning. Can you provide me with the current state-of-the-art in this field?</p>

<reinforcement-learning><meta-learning>
What are the state-of-the-art meta-reinforcement learning methods?
Sara El
# What are the state-of-the-art meta-reinforcement learning methods?

(Asked by: username_0 on 2020-02-08)

This question can seem a little bit too broad, but I am wondering what are the current state-of-the-art works on meta reinforcement learning. Can you provide me with the current state-of-the-art in this field?

**Tags:** reinforcement-learning, meta-learning


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## 2 answers

### Answer by username_2 on 2020-02-08. Score: 3 | Accepted answer

One of the most recent papers on meta-RL is [meta-Q-learning](https://arxiv.org/pdf/1910.00125.pdf). This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-reinforcement learning (meta-RL). MQL builds upon three simple ideas.

* Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory.
* Using a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies.
* past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates

Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with state-of-the-art meta-RL algorithms.

I think that other references to other work on meta-RL are present in the experiments part of the MQL paper.


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### Answer by username_1 on 2021-02-22. Score: 1

Meta-Reinforcement Learning can refer to a broad range of ideas. Also, different algorithms are SOTA under different evaluation metrics (sample efficiency, agent performance, adaptation speed on a new task, etc)

Assuming that you are referring to the problem of quickly learning/adapting to a new task by training an agent on a distribution of related tasks, the following are some popular algorithms

* PEARL [Rakelly et al., 2019]
* VariBAD [Zintgraf et al., 2020]
* Meta-Q-Learning [Fakoor et al., 2020]

**References:**

1. K Rakelly, A Xhou, D Quillen, C Finn, S Levine - [Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables](https://arxiv.org/abs/1903.08254), ICML 2019.
2. L Zintgraf et al., - [VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning](https://arxiv.org/abs/1910.08348), ICLR 2020.
3. R Fakoor, P Chaudhari, S Soatto, A J Smola - [Meta-Q-Learning](https://arxiv.org/abs/1910.00125), ICLR 2020.


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