7 research outputs found
Learning in Non-Cooperative Configurable Markov Decision Processes
The Configurable Markov Decision Process framework includes two entities: a Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance. This presupposes that the two actors have the same reward functions. What if the configurator does not have the same intentions as the agent? This paper introduces the Non-Cooperative Configurable Markov Decision Process, a setting that allows having two (possibly different) reward functions for the configurator and the agent. Then, we consider an online learning problem, where the configurator has to find the best among a finite set of possible configurations. We propose two learning algorithms to minimize the configurator's expected regret, which exploits the problem's structure, depending on the agent's feedback. While a naive application of the UCB algorithm yields a regret that grows indefinitely over time, we show that our approach suffers only bounded regret. Furthermore, we empirically show the performance of our algorithm in simulated domains
Sequential Transfer in Reinforcement Learning with a Generative Model
We are interested in how to design reinforcement learning agents that
provably reduce the sample complexity for learning new tasks by transferring
knowledge from previously-solved ones. The availability of solutions to related
problems poses a fundamental trade-off: whether to seek policies that are
expected to achieve high (yet sub-optimal) performance in the new task
immediately or whether to seek information to quickly identify an optimal
solution, potentially at the cost of poor initial behavior. In this work, we
focus on the second objective when the agent has access to a generative model
of state-action pairs. First, given a set of solved tasks containing an
approximation of the target one, we design an algorithm that quickly identifies
an accurate solution by seeking the state-action pairs that are most
informative for this purpose. We derive PAC bounds on its sample complexity
which clearly demonstrate the benefits of using this kind of prior knowledge.
Then, we show how to learn these approximate tasks sequentially by reducing our
transfer setting to a hidden Markov model and employing spectral methods to
recover its parameters. Finally, we empirically verify our theoretical findings
in simple simulated domains.Comment: ICML 202