Reinforcement Learning with Trajectory Feedback

Understand

The standard feedback model of reinforcement learning requires revealing the reward of every visited state-action pair.

• However, in practice, it is often the case that such frequent feedback is not available.
• In this work, we take a first step towards relaxing this assumption and require a weaker form of feedback, which we refer to as \emph{trajectory feedback}.
• Instead of observing the reward obtained after every action, we assume we only receive a score that represents the quality of the whole trajectory observed by the agent, namely, the sum of all rewards obtained over this trajectory.