At inference time, the loop is simple:

Step 1

Set target return

Choose the return you want the trajectory to achieve, such as $R_0$.

->

Step 2

Predict action

Use return, current state, and history to predict the next action.

->

Step 3

Environment responds

The action produces the next state and reward.

->

Step 4

Update and repeat

Adjust the remaining target return, append the new context, and continue.

Loop: new state + reward become part of the next prediction context

Decision Transformer inference repeatedly conditions on the desired return and accumulated history.

This can feel like "RL without RL": the training objective is supervised action prediction, but the conditioning variable lets the user ask for higher-return behavior when the dataset supports it.

Hierarchical RL