23,365 research outputs found
From supply chains to demand networks. Agents in retailing: the electrical bazaar
A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
DoShiCo Challenge: Domain Shift in Control Prediction
Training deep neural network policies end-to-end for real-world applications
so far requires big demonstration datasets in the real world or big sets
consisting of a large variety of realistic and closely related 3D CAD models.
These real or virtual data should, moreover, have very similar characteristics
to the conditions expected at test time. These stringent requirements and the
time consuming data collection processes that they entail, are currently the
most important impediment that keeps deep reinforcement learning from being
deployed in real-world applications. Therefore, in this work we advocate an
alternative approach, where instead of avoiding any domain shift by carefully
selecting the training data, the goal is to learn a policy that can cope with
it. To this end, we propose the DoShiCo challenge: to train a model in very
basic synthetic environments, far from realistic, in a way that it can be
applied in more realistic environments as well as take the control decisions on
real-world data. In particular, we focus on the task of collision avoidance for
drones. We created a set of simulated environments that can be used as
benchmark and implemented a baseline method, exploiting depth prediction as an
auxiliary task to help overcome the domain shift. Even though the policy is
trained in very basic environments, it can learn to fly without collisions in a
very different realistic simulated environment. Of course several benchmarks
for reinforcement learning already exist - but they never include a large
domain shift. On the other hand, several benchmarks in computer vision focus on
the domain shift, but they take the form of a static datasets instead of
simulated environments. In this work we claim that it is crucial to take the
two challenges together in one benchmark.Comment: Published at SIMPAR 2018. Please visit the paper webpage for more
information, a movie and code for reproducing results:
https://kkelchte.github.io/doshic
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