1 research outputs found
Reinforcement Learning with Feedback Graphs
We study episodic reinforcement learning in Markov decision processes when
the agent receives additional feedback per step in the form of several
transition observations. Such additional observations are available in a range
of tasks through extended sensors or prior knowledge about the environment
(e.g., when certain actions yield similar outcome). We formalize this setting
using a feedback graph over state-action pairs and show that model-based
algorithms can leverage the additional feedback for more sample-efficient
learning. We give a regret bound that, ignoring logarithmic factors and
lower-order terms, depends only on the size of the maximum acyclic subgraph of
the feedback graph, in contrast with a polynomial dependency on the number of
states and actions in the absence of a feedback graph. Finally, we highlight
challenges when leveraging a small dominating set of the feedback graph as
compared to the bandit setting and propose a new algorithm that can use
knowledge of such a dominating set for more sample-efficient learning of a
near-optimal policy