18,103 research outputs found
Bayesian Reinforcement Learning via Deep, Sparse Sampling
We address the problem of Bayesian reinforcement learning using efficient
model-based online planning. We propose an optimism-free Bayes-adaptive
algorithm to induce deeper and sparser exploration with a theoretical bound on
its performance relative to the Bayes optimal policy, with a lower
computational complexity. The main novelty is the use of a candidate policy
generator, to generate long-term options in the planning tree (over beliefs),
which allows us to create much sparser and deeper trees. Experimental results
on different environments show that in comparison to the state-of-the-art, our
algorithm is both computationally more efficient, and obtains significantly
higher reward in discrete environments.Comment: Published in AISTATS 202
Rational bidding using reinforcement learning: an application in automated resource allocation
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems.
The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized
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