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We study a variant of the contextual bandit problem where an agent can intervene through a set of stochastic expert policies. Given a fixed context, each expert samples actions from a fixed conditional distribution. The agent seeks to remain competitive with the “best” among the given set of experts. We propose the Divergence-based Upper Confidence Bound (D-UCB) algorithm that uses importance sampling to share information across experts and provide horizon-independent constant regret bounds that only scale linearly in the number of experts. We also provide the Empirical D-UCB (ED-UCB) algorithm that can function with only approximate knowledge of expert distributions. Further, we investigate the episodic setting where the agent interacts with an environment that changes over episodes. Each episode can have different context and reward distributions resulting in the best expert changing across episodes. We show that by bootstrapping from\(\mathcal {O}(N\log (NT^2\sqrt {E}))\)samples, ED-UCB guarantees a regret that scales as\(\mathcal {O}(E(N+1) + \frac{N\sqrt {E}}{T^2})\)forNexperts overEepisodes, each of lengthT. We finally empirically validate our findings through simulations.
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Settling the Sample Complexity of Online Reinforcement Learning
A central issue lying at the heart of online reinforcement learning (RL) is data efficiency. While a number of recent works achieved asymptotically minimal regret in online RL, the optimality of these results is only guaranteed in a “large-sample” regime, imposing enormous burn-in cost in order for their algorithms to operate optimally. How to achieve minimax-optimal regret without incurring any burn-in cost has been an open problem in RL theory. We settle this problem for finite-horizon inhomogeneous Markov decision processes. Specifically, we prove that a modified version ofMVP(Monotonic Value Propagation), an optimistic model-based algorithm proposed by Zhang et al. [82], achieves a regret on the order of (modulo log factors)\begin{equation*} \min \big \lbrace \sqrt {SAH^3K}, \,HK \big \rbrace,\end{equation*}whereSis the number of states,Ais the number of actions,His the horizon length, andKis the total number of episodes. This regret matches the minimax lower bound for the entire range of sample sizeK≥ 1, essentially eliminating any burn-in requirement. It also translates to a PAC sample complexity (i.e., the number of episodes needed to yield ε-accuracy) of\(\frac{SAH^3}{\varepsilon ^2} \)up to log factor, which is minimax-optimal for the full ε-range. Further, we extend our theory to unveil the influences of problem-dependent quantities like the optimal value/cost and certain variances. The key technical innovation lies in a novel analysis paradigm (based on a new concept called “profiles”) to decouple complicated statistical dependency across the sample trajectories — a long-standing challenge facing the analysis of online RL in the sample-starved regime.
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- PAR ID:
- 10625608
- Publisher / Repository:
- Journal of the ACM
- Date Published:
- Journal Name:
- Journal of the ACM
- ISSN:
- 0004-5411
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1145/3733592
