42,140 research outputs found
CompILE: Compositional Imitation Learning and Execution
We introduce Compositional Imitation Learning and Execution (CompILE): a
framework for learning reusable, variable-length segments of
hierarchically-structured behavior from demonstration data. CompILE uses a
novel unsupervised, fully-differentiable sequence segmentation module to learn
latent encodings of sequential data that can be re-composed and executed to
perform new tasks. Once trained, our model generalizes to sequences of longer
length and from environment instances not seen during training. We evaluate
CompILE in a challenging 2D multi-task environment and a continuous control
task, and show that it can find correct task boundaries and event encodings in
an unsupervised manner. Latent codes and associated behavior policies
discovered by CompILE can be used by a hierarchical agent, where the high-level
policy selects actions in the latent code space, and the low-level,
task-specific policies are simply the learned decoders. We found that our
CompILE-based agent could learn given only sparse rewards, where agents without
task-specific policies struggle.Comment: ICML (2019
Formal Estimation of Collision Risks for Autonomous Vehicles: A Compositional Data-Driven Approach
In this work, we propose a compositional data-driven approach for the formal
estimation of collision risks for autonomous vehicles (AVs) while acting in a
stochastic multi-agent framework. The proposed approach is based on the
construction of sub-barrier certificates for each stochastic agent via a set of
data collected from its trajectories while providing an a-priori guaranteed
confidence on the data-driven estimation. In our proposed setting, we first
cast the original collision risk problem for each agent as a robust
optimization program (ROP). Solving the acquired ROP is not tractable due to an
unknown model that appears in one of its constraints. To tackle this
difficulty, we collect finite numbers of data from trajectories of each agent
and provide a scenario optimization program (SOP) corresponding to the original
ROP. We then establish a probabilistic bridge between the optimal value of SOP
and that of ROP, and accordingly, we formally construct the sub-barrier
certificate for each unknown agent based on the number of data and a required
level of confidence. We then propose a compositional technique based on
small-gain reasoning to quantify the collision risk for multi-agent AVs with
some desirable confidence based on sub-barrier certificates of individual
agents constructed from data. For the case that the proposed compositionality
conditions are not satisfied, we provide a relaxed version of compositional
results without requiring any compositionality conditions but at the cost of
providing a potentially conservative collision risk. Eventually, we also
present our approaches for non-stochastic multi-agent AVs. We demonstrate the
effectiveness of our proposed results by applying them to a vehicle platooning
consisting of 100 vehicles with 1 leader and 99 followers. We formally estimate
the collision risk by collecting data from trajectories of each agent.Comment: This work has been accepted at IEEE Transactions on Control of
Network System
Decentralized bisimulation for multiagent systems
Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems. The notion of bisimulation has been introduced as a powerful way to abstract from details of systems in the formal verification community. When applying to multiagent systems, classical bisimulations will allow one agent to make decisions based on full histories of others. Thus, as a general concept, classical bisimulations are unrealistically powerful for such systems. In this paper, we define a coarser notion of bisimulation under which an agent can only make realistic decisions based on information available to it. Our bisimulation still implies trace distribution equivalence of the systems, and moreover, it allows a compositional abstraction framework of reasoning about the systems
A Compositional Framework for Preference-Aware Agents
A formal description of a Cyber-Physical system should include a rigorous
specification of the computational and physical components involved, as well as
their interaction. Such a description, thus, lends itself to a compositional
model where every module in the model specifies the behavior of a
(computational or physical) component or the interaction between different
components. We propose a framework based on Soft Constraint Automata that
facilitates the component-wise description of such systems and includes the
tools necessary to compose subsystems in a meaningful way, to yield a
description of the entire system. Most importantly, Soft Constraint Automata
allow the description and composition of components' preferences as well as
environmental constraints in a uniform fashion. We illustrate the utility of
our framework using a detailed description of a patrolling robot, while
highlighting methods of composition as well as possible techniques to employ
them.Comment: In Proceedings V2CPS-16, arXiv:1612.0402
Projective simulation for artificial intelligence
We propose a model of a learning agent whose interaction with the environment
is governed by a simulation-based projection, which allows the agent to project
itself into future situations before it takes real action. Projective
simulation is based on a random walk through a network of clips, which are
elementary patches of episodic memory. The network of clips changes
dynamically, both due to new perceptual input and due to certain compositional
principles of the simulation process. During simulation, the clips are screened
for specific features which trigger factual action of the agent. The scheme is
different from other, computational, notions of simulation, and it provides a
new element in an embodied cognitive science approach to intelligent action and
learning. Our model provides a natural route for generalization to
quantum-mechanical operation and connects the fields of reinforcement learning
and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes
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