Body: <p>Are there methods (possibly logical or (how they are called in the literature) relational) that allows for the developmental systems to understand or explain the value of the received reward during the developmental process. E.g. if the system (agent) can understand that reward is by the chance, then it should be processed quite differently than the reward that is just initial downpayment for the expected series of rewards. <strong><em>Credit assignment</em> is the one method (especially for delayed rewards), but maybe there are different methods as well?</strong></p>
  
  <p>Relational reinforcement learning allows to learn symbolic transition and reward functions and emerging understanding of the reward by the agent can greatly facilitate the inner consciousness of the agent and the search process for the best transition and reward functions (symbolic search space can be enormous).</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-20T23:39:20.780Z
Comments.Text: Please define or give at least an example of "trying to explain". Meta-explanation is still a research topic. [J.Pitrat's blog](http://bootstrappingartificialintelligence.fr/WordPress3/) could give some interesting insights about it.
Comments.UserDisplayName: Basile Starynkevitch
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-21T07:17:33.037Z
Comments.Text: By "trying to explain" I mean that the system (agent) have some kind of function (reward value, current state of environment) => ((state, action)=>(reward), whether and how to update the transition, reward function), i.e. function that observer the state and reward value and tries to explicitly model the environment and modeling is done by specifying and updating function.
Comments.UserDisplayName: TomR
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-21T08:07:44.343Z
Comments.Text: That should go into your question*
Comments.UserDisplayName: Basile Starynkevitch
Tags: <reinforcement-learning><logic><artificial-consciousness><rewards><symbolic-ai>
Title: Developmental systems that try to explain or understand the reward value in the reinforcement learning?
UserDisplayName: TomR
ThreadText: # Developmental systems that try to explain or understand the reward value in the reinforcement learning?
  
  (Asked by: username_0 on 2019-08-20)
  
  Are there methods (possibly logical or (how they are called in the literature) relational) that allows for the developmental systems to understand or explain the value of the received reward during the developmental process. E.g. if the system (agent) can understand that reward is by the chance, then it should be processed quite differently than the reward that is just initial downpayment for the expected series of rewards. ***Credit assignment* is the one method (especially for delayed rewards), but maybe there are different methods as well?**
  
  Relational reinforcement learning allows to learn symbolic transition and reward functions and emerging understanding of the reward by the agent can greatly facilitate the inner consciousness of the agent and the search process for the best transition and reward functions (symbolic search space can be enormous).
  
  **Tags:** reinforcement-learning, logic, artificial-consciousness, rewards, symbolic-ai
  > **Comments:**
  > **username_1**: Please define or give at least an example of "trying to explain". Meta-explanation is still a research topic. [J.Pitrat's blog](http://bootstrappingartificialintelligence.fr/WordPress3/) could give some interesting insights about it.  
  > **username_0**: By "trying to explain" I mean that the system (agent) have some kind of function (reward value, current state of environment) => ((state, action)=>(reward), whether and how to update the transition, reward function), i.e. function that observer the state and reward value and tries to explicitly model the environment and modeling is done by specifying and updating function.  
  > **username_1**: That should go into your question*  
  
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  ## 0 answers
  
  