# Why the optimal Bellman operator of a Q-function can be approximated by a single point

I am currently studying reinforcement learning, especially DQN. In DQN, learning proceeds in such a way as to minimize the norm (least-squares, Huber, etc.) of the optimal Bellman equation and the approximate Q-function as follows (roughly): $$\min\|B^*Q^*-\hat{Q}\|.$$ Here $$\hat{Q}$$ is an estimator of Q function, $$Q^*$$ is the optimal Q function, and $$B^*$$ is the optimal Bellman operator. $$B^*Q^*(s,a)=\sum_{s'}p_T(s'|s,a)[r(s,a,s')+\gamma \max_{a'}Q^*(s',a')],$$ where $$p_T$$ is a transition probability, $$r$$ is an immediate reward, and $$\gamma$$ is a discount factor. As I understand it, in the DQN algorithm, the optimal Bellman equation is approximated by a single point, and the optimal Q function $$Q^*$$ is further approximated by an estimator different from $$\hat{Q}$$, say $$\tilde{Q}$$. $$$$\label{question} B^*Q^*(s,a)\approx r(s,a,s')+\gamma\max_{a'}Q^*(s',a')\approx r(s,a,s')+\gamma\max_{a'}\tilde{Q}(s',a'),\tag{*}$$$$ therefore the problem becomes as follows: $$\min\|r(s,a,s')+\gamma\max_{a'}\tilde{Q}(s',a')-\hat{Q}(s,a)\|.$$

What I want to ask： I would like to know the mathematical or theoretical background of the approximation of \eqref{question}, especially why the first approximation is possible. It looks like a very rough approximation. Can the right-hand side be defined as an "approximate Bellman equation"?　I have looked at various literature and online resources, but none of them mention exact derivation, so I would be very grateful if you could tell me about reference as well.

• Hi. Can you please edit your post to include a link to the research paper or book where you took these equations from, in order to provide some context? – nbro Apr 9 at 13:12

Both your notation and terminology are quite confusing. For example, I'm not sure what is an "optimal" Bellman operator is. Here's a good clarification on definition of a Bellman operator. Likewise, your description of the DQN algorithm completely ignores the averaging over states/actions/rewards sampled from the replay memory.

Trying to savage your notation, I'll introduce a Bellman operator $$B$$ that acts on any state-value function $$Q$$ as:

$$B[Q(s,a)] = \mathbb{E}_{s'}\left[r(s,a,s') + \gamma \max_{a'} Q(s',a') \right]$$

And optimal Q function $$Q^*$$ satisfies the Bellman equation:

$$B[Q^*(s,a)] = Q^*(s,a)$$

The value-iteration algorithm iteratively applies the Bellman operator: $$Q^{n+1}(s,a) = B[Q^n(s,a)]$$ And is proven to converge to the optimal Q function: $$Q^*(s,a) = \lim_{n\to\infty}Q^{n}(s,a)$$

Now, in the DQN algorithm we are approximating Q functions with DNNs with parameters $$\theta$$: $$Q(s,a; \theta)$$. Then we approximate the value-iteration algorithm by minimizing the norm

$$\mathbb{E}_{s,a} \left\|B[Q(s,a; \theta_{i-1})] - Q(s,a; \theta_i)\right\|$$

The minimization is performed over parameters $$\theta_i$$ with previous parameters $$\theta_{i-1}$$ held fixed.

The averages $$\mathbb{E}_{s,a}$$ and $$\mathbb{E}_{s'}$$ are approximated by sampling a minibatch $$MB$$ of $$(s,a,r,s')$$ tuples from the replay memory.

I suppose that is the closest I can get to the crux of your question. You are claiming that the average in the bellman operator application is approximated by a single point: $$B[Q(s,a)] \simeq r(s,a,s') + \gamma \max_{a'} Q(s',a')$$ While, in fact it is approximated by $$B[Q(s,a)] \simeq \mathbb{E}_{s'\in MB} \left[ r(s,a,s') + \gamma \max_{a'} Q(s',a')\right]$$