3,252 research outputs found
Scalable Planning and Learning for Multiagent POMDPs: Extended Version
Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the number of agents. To combat this intractability, we propose a novel
scalable approach based on sample-based planning and factored value functions
that exploits structure present in many multiagent settings. This approach
applies not only in the planning case, but also in the Bayesian reinforcement
learning setting. Experimental results show that we are able to provide high
quality solutions to large multiagent planning and learning problems
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
With advances in reinforcement learning (RL), agents are now being developed
in high-stakes application domains such as healthcare and transportation.
Explaining the behavior of these agents is challenging, as the environments in
which they act have large state spaces, and their decision-making can be
affected by delayed rewards, making it difficult to analyze their behavior. To
address this problem, several approaches have been developed. Some approaches
attempt to convey the behavior of the agent, describing the
actions it takes in different states. Other approaches devised
explanations which provide information regarding the agent's decision-making in
a particular state. In this paper, we combine global and local explanation
methods, and evaluate their joint and separate contributions, providing (to the
best of our knowledge) the first user study of combined local and global
explanations for RL agents. Specifically, we augment strategy summaries that
extract important trajectories of states from simulations of the agent with
saliency maps which show what information the agent attends to. Our results
show that the choice of what states to include in the summary (global
information) strongly affects people's understanding of agents: participants
shown summaries that included important states significantly outperformed
participants who were presented with agent behavior in a randomly set of chosen
world-states. We find mixed results with respect to augmenting demonstrations
with saliency maps (local information), as the addition of saliency maps did
not significantly improve performance in most cases. However, we do find some
evidence that saliency maps can help users better understand what information
the agent relies on in its decision making, suggesting avenues for future work
that can further improve explanations of RL agents
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