<p>Randomness can be handled by replacing single average reward output with a distribution with possible values. By introducing a new learning rule, reflecting the transition from Bellman’s (average) equation to its distributional counterpart, <a href="https://arxiv.org/pdf/1707.06887.pdf" rel="nofollow noreferrer">the Value distribution approach</a> has been able to surpass the performance of all other comparable approaches.</p>
<p><a href="https://www.deepmind.com/blog/going-beyond-average-for-reinforcement-learning" rel="nofollow noreferrer">https://www.deepmind.com/blog/going-beyond-average-for-reinforcement-learning</a></p>
Is reinforcement learning applicable to a RANDOM environment?
<p>I have a <strong>fundamental</strong> question on the applicability of reinforcement learning (RL) on a problem we are trying to solve.</p>

<p>We are trying to use RL for inventory management - where the demand is entirely <strong>random</strong> (it probably has a pattern in real life but for now let us assume that we have been forced to treat as purely random). </p>

<p>As I understand, RL can help learn how to play a game (say chess) or help a robot learn to walk. <em>But</em> all games have <strong>rules</strong> and so does the ‘cart-pole’ (of OpenAI Gym) – there are rules of ‘physics’ that govern when the cart-pole will tip and fall over. </p>

<p>For our problem there are no rules – the environment changes randomly (demand made for the product).</p>

<p>Is RL really applicable to such situations?</p>

<p>If it does - then what will improve the performance?</p>

<p>Further details: 
- The only two stimuli available from the ‘environment’ are the currently available level of product 'X' and the current demand 'Y'
- And the ‘action’ is binary - do I order a quantity 'Q' to refill or do I not (discrete action space).
- We are using DQN and an Adam optimizer.</p>

<p>Our results are poor - I admit I have trained only for about 5,000 or 10,000 - should I let it train on for days because it is a random environment?</p>

<p>thank you
Rajesh</p>