Body: <p>I'm working on a real problem with continuous large actions range, where my agent takes actions based only on the current state of the environment, transitioning to a future state that is unrelated to the next current state, the only thing to take into account is the reward generated by the action taken by the agent. Is there any RL method that applies to this kind of problem?</p>
  <p>So, clarifying the problem better... From the current observation (s_t), the agent takes actions reaching a new state (s_t+1). This new state is indeed a function of the previous state (s_t) and of the actions (a_t) taken by the agent. However, this next state (s_t+1) is not the next observation that the agent will take into account to take new actions, being only useful for calculating the reward in that iteration. My question consists of the possibility that RL can be applied to this type of problem, where the state reached by the agent's actions in a given instant of time is not my next observation that the agent will take into account to take the next actions.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-28T07:33:07.110Z
Comments.Text: Can you clarify - does the action taken influence the next state at all? (You say that the next state is unrelated to the current state, but don't say anything about the action)
Comments.UserDisplayName: Neil Slater
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-28T10:24:07.220Z
Comments.Text: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-29T17:02:56.250Z
Comments.Text: Hi Neil, thanks for the reply! Tried to clarify it better on the question...
Comments.UserDisplayName: MaarcosNascimen
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-29T19:05:07.977Z
Comments.Text: Inspired from quantum mechanics, can you find or approximate some operator acting on the next state to obtain the next observable? Is so then you can still arrive at some state-action pair table or function (approximator).
Comments.UserDisplayName: cinch
Tags: <reinforcement-learning><deep-learning><policy-gradients>
Title: Reinforcement Learning - Independence between current state and future state
UserDisplayName: MaarcosNascimen
ThreadText: # Reinforcement Learning - Independence between current state and future state
  
  (Asked by: username_0 on 2022-11-28)
  
  I'm working on a real problem with continuous large actions range, where my agent takes actions based only on the current state of the environment, transitioning to a future state that is unrelated to the next current state, the only thing to take into account is the reward generated by the action taken by the agent. Is there any RL method that applies to this kind of problem?
  
  So, clarifying the problem better... From the current observation (s\_t), the agent takes actions reaching a new state (s\_t+1). This new state is indeed a function of the previous state (s\_t) and of the actions (a\_t) taken by the agent. However, this next state (s\_t+1) is not the next observation that the agent will take into account to take new actions, being only useful for calculating the reward in that iteration. My question consists of the possibility that RL can be applied to this type of problem, where the state reached by the agent's actions in a given instant of time is not my next observation that the agent will take into account to take the next actions.
  
  **Tags:** reinforcement-learning, deep-learning, policy-gradients
  > **Comments:**
  > **username_1**: Can you clarify - does the action taken influence the next state at all? (You say that the next state is unrelated to the current state, but don't say anything about the action)  
  > **username_2**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
  > **username_0**: Hi Neil, thanks for the reply! Tried to clarify it better on the question...  
  > **username_3**: Inspired from quantum mechanics, can you find or approximate some operator acting on the next state to obtain the next observable? Is so then you can still arrive at some state-action pair table or function (approximator).  
  
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  ## 0 answers
  
  