Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-22T20:30:52.260Z
Answers.LastActivityDate: 2021-02-22T20:30:52.260Z
Answers.UserDisplayName: Mohith Kaameswaran Sakthivel
Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-08T11:30:22.463Z
Answers.LastActivityDate: 2021-02-23T10:19:43.790Z
Answers.LastEditDate: 2021-02-23T10:19:43.790Z
Answers.UserDisplayName: hola
Body: <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>
  
Tags: <reinforcement-learning><meta-learning>
Title: What are the state-of-the-art meta-reinforcement learning methods?
UserDisplayName: Sara El
ThreadText: # 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
  
  
  ---
  
  ## 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.
  
  
  ---
  
  ### 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.
  
  
  ---
  
  