526 research outputs found
Reinforcement learning with limited reinforcement: Using Bayes risk for active learning in POMDPs
Acting in domains where an agent must plan several steps ahead to achieve a goal can be a challenging task, especially if the agentʼs sensors provide only noisy or partial information. In this setting, Partially Observable Markov Decision Processes (POMDPs) provide a planning framework that optimally trades between actions that contribute to the agentʼs knowledge and actions that increase the agentʼs immediate reward. However, the task of specifying the POMDPʼs parameters is often onerous. In particular, setting the immediate rewards to achieve a desired balance between information-gathering and acting is often not intuitive.
In this work, we propose an approximation based on minimizing the immediate Bayes risk for choosing actions when transition, observation, and reward models are uncertain. The Bayes-risk criterion avoids the computational intractability of solving a POMDP with a multi-dimensional continuous state space; we show it performs well in a variety of problems. We use policy queries—in which we ask an expert for the correct action—to infer the consequences of a potential pitfall without experiencing its effects. More important for human–robot interaction settings, policy queries allow the agent to learn the reward model without the reward values ever being specified
Barrier Functions for Multiagent-POMDPs with DTL Specifications
Multi-agent partially observable Markov decision processes (MPOMDPs) provide a framework to represent heterogeneous autonomous agents subject to uncertainty and partial observation. In this paper, given a nominal policy provided by a human operator or a conventional planning method, we propose a technique based on barrier functions to design a minimally interfering safety-shield ensuring satisfaction of high-level specifications in terms of linear distribution temporal logic (LDTL). To this end, we use sufficient and necessary conditions for the invariance of a given set based on discrete-time barrier functions (DTBFs) and formulate sufficient conditions for finite time DTBF to study finite time convergence to a set. We then show that different LDTL mission/safety specifications can be cast as a set of invariance or finite time reachability problems. We demonstrate that the proposed method for safety-shield synthesis can be implemented online by a sequence of one-step greedy algorithms. We demonstrate the efficacy of the proposed method using experiments involving a team of robots
Formal Modelling for Multi-Robot Systems Under Uncertainty
Purpose of Review: To effectively synthesise and analyse multi-robot
behaviour, we require formal task-level models which accurately capture
multi-robot execution. In this paper, we review modelling formalisms for
multi-robot systems under uncertainty, and discuss how they can be used for
planning, reinforcement learning, model checking, and simulation.
Recent Findings: Recent work has investigated models which more accurately
capture multi-robot execution by considering different forms of uncertainty,
such as temporal uncertainty and partial observability, and modelling the
effects of robot interactions on action execution. Other strands of work have
presented approaches for reducing the size of multi-robot models to admit more
efficient solution methods. This can be achieved by decoupling the robots under
independence assumptions, or reasoning over higher level macro actions.
Summary: Existing multi-robot models demonstrate a trade off between
accurately capturing robot dependencies and uncertainty, and being small enough
to tractably solve real world problems. Therefore, future research should
exploit realistic assumptions over multi-robot behaviour to develop smaller
models which retain accurate representations of uncertainty and robot
interactions; and exploit the structure of multi-robot problems, such as
factored state spaces, to develop scalable solution methods.Comment: 23 pages, 0 figures, 2 tables. Current Robotics Reports (2023). This
version of the article has been accepted for publication, after peer review
(when applicable) but is not the Version of Record and does not reflect
post-acceptance improvements, or any corrections. The Version of Record is
available online at: https://dx.doi.org/10.1007/s43154-023-00104-
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