Answers.Body: <p>Meta Learning can mean many things, but at the core is about having a second layer of optimisation, beside the usual one needed to solve your task.</p>
  <p>For instance, in RL for robotics you may have a SAC (Soft Actor Critic) agent that learns how to pick and place, by first initialising a random neural network and then learning which weights minimise a loss function related to successful picks. Given this architecture you can fix a meta-goal, for instance being precise (base-goal) and fast (meta-goal) at picking. Or maximising human safety, minimising robot wear, and so on.</p>
  <p>Now you can meta-learn the best meta-parameters to achieve this meta-goal. Example of meta-parameters could be the network initialisation, the shape of the loss function, the network architecture, etc.</p>
  <p>Check out Meta-Learning in Neural Networks: A Survey     <a href="https://arxiv.org/abs/2004.05439" rel="nofollow noreferrer">https://arxiv.org/abs/2004.05439</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-12-10T16:02:36.433Z
Answers.LastActivityDate: 2021-12-10T16:02:36.433Z
Answers.UserDisplayName: Rexcirus
Body: <p>We can use DDPG to train agents to stack objects. And stacking objects can be viewed as first grasping followed by pick and place. In this context, how does meta-reinforcement learning fit? Does it mean I can use grasp, pick and place as training tasks and generalize to assembling objects?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-10-14T16:03:04.023Z
Comments.Text: Do you have more context? For example, were you reading some an article about meta-learning and RL? If yes, please, edit your post and include references to the source. I am asking you this because I know of at least one paper where meta-learning comes into play, but I don't know if this is what you're referring to.
Comments.UserDisplayName: nbro
Tags: <reinforcement-learning><definitions><meta-learning>
Title: What exactly does meta-learning in reinforcement learning setting mean?
UserDisplayName: Sofi
ThreadText: # What exactly does meta-learning in reinforcement learning setting mean?
  
  (Asked by: username_0 on 2020-10-14)
  
  We can use DDPG to train agents to stack objects. And stacking objects can be viewed as first grasping followed by pick and place. In this context, how does meta-reinforcement learning fit? Does it mean I can use grasp, pick and place as training tasks and generalize to assembling objects?
  
  **Tags:** reinforcement-learning, definitions, meta-learning
  
  > **Comments:**
  > **username_1**: Do you have more context? For example, were you reading some an article about meta-learning and RL? If yes, please, edit your post and include references to the source. I am asking you this because I know of at least one paper where meta-learning comes into play, but I don't know if this is what you're referring to.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2021-12-10. Score: 0
  
  Meta Learning can mean many things, but at the core is about having a second layer of optimisation, beside the usual one needed to solve your task.
  
  For instance, in RL for robotics you may have a SAC (Soft Actor Critic) agent that learns how to pick and place, by first initialising a random neural network and then learning which weights minimise a loss function related to successful picks. Given this architecture you can fix a meta-goal, for instance being precise (base-goal) and fast (meta-goal) at picking. Or maximising human safety, minimising robot wear, and so on.
  
  Now you can meta-learn the best meta-parameters to achieve this meta-goal. Example of meta-parameters could be the network initialisation, the shape of the loss function, the network architecture, etc.
  
  Check out Meta-Learning in Neural Networks: A Survey <https://arxiv.org/abs/2004.05439>
  
  
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