Body: <p>I understand that a stochastic environment is one that does not always lead you to the desired state by giving a particular action <span class="math-container">$a$</span> (But the probability to change to a not desire state is fixed, right?).</p>
  <p>For example, the frozen lake environment is a stochastic environment. Sometimes you want to move in one direction and the agent slips and moves in another direction. Unlike an environment with multiple agents that the probability of the actions of the other agents is changing because they keep learning (a non-stationary environment).</p>
  <p>Why is it difficult to learn in a stochastic environment, if, for example, Q-learning can solve the frozen lake environment? In what cases would it be difficult to learn in a stochastic environment?</p>
  <p>I have found some <a href="https://arxiv.org/abs/1902.04178" rel="nofollow noreferrer">articles</a> that address that issue, but I don't understand why it would be difficult if Q-learning can solve it (for discrete states/actions).</p>
  
Tags: <reinforcement-learning><q-learning><markov-decision-process><environment>
Title: Is it really hard to learn in a stochastic environment?
UserDisplayName: Pulse9
ThreadText: # Is it really hard to learn in a stochastic environment?
  
  (Asked by: username_0 on 2021-08-26)
  
  I understand that a stochastic environment is one that does not always lead you to the desired state by giving a particular action $a$ (But the probability to change to a not desire state is fixed, right?).
  
  For example, the frozen lake environment is a stochastic environment. Sometimes you want to move in one direction and the agent slips and moves in another direction. Unlike an environment with multiple agents that the probability of the actions of the other agents is changing because they keep learning (a non-stationary environment).
  
  Why is it difficult to learn in a stochastic environment, if, for example, Q-learning can solve the frozen lake environment? In what cases would it be difficult to learn in a stochastic environment?
  
  I have found some [articles](https://arxiv.org/abs/1902.04178) that address that issue, but I don't understand why it would be difficult if Q-learning can solve it (for discrete states/actions).
  
  **Tags:** reinforcement-learning, q-learning, markov-decision-process, environment
  
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  ## 0 answers
  
  