In Richard Sutton's book on RL (2nd edition), he presents the Dyna-Q algorithm, which combines planning and learning.
In the planning part of the algorithm, the Dyna-agent randomly samples n state-action pairs $(s, a)$ previously seen by the agent, feeds this pair into its model of the environment and gets a sampled next state $s'$ and reward $r$. It then uses this set $(s,a,r,s')$ to perform its usual Q-learning update.
In a deterministic environment, the reward and next state are always the same for a given state-action pair $(s_t,a_t)\to(r_{t+1},s_{t+1}')$. In his chapter on Dyna-Q, Sutton does not refer to this process as being a form of experience replay, and only introduces the latter concept much later in the book. However I can't really see the distinction (if there is one) between those two processes.
Is it correct to say that in a deterministic environment, planning in Tabular Dyna-Q is a form of experience replay ?