7,857 research outputs found
Sleeping Beauty Reconsidered: Conditioning and Reflection in Asynchronous Systems
A careful analysis of conditioning in the Sleeping Beauty problem is done,
using the formal model for reasoning about knowledge and probability developed
by Halpern and Tuttle. While the Sleeping Beauty problem has been viewed as
revealing problems with conditioning in the presence of imperfect recall, the
analysis done here reveals that the problems are not so much due to imperfect
recall as to asynchrony. The implications of this analysis for van Fraassen's
Reflection Principle and Savage's Sure-Thing Principle are considered.Comment: A preliminary version of this paper appears in Principles of
Knowledge Representation and Reasoning: Proceedings of the Ninth
International Conference (KR 2004). This version will appear in Oxford
Studies in Epistemolog
Arena: A General Evaluation Platform and Building Toolkit for Multi-Agent Intelligence
Learning agents that are not only capable of taking tests, but also
innovating is becoming a hot topic in AI. One of the most promising paths
towards this vision is multi-agent learning, where agents act as the
environment for each other, and improving each agent means proposing new
problems for others. However, existing evaluation platforms are either not
compatible with multi-agent settings, or limited to a specific game. That is,
there is not yet a general evaluation platform for research on multi-agent
intelligence. To this end, we introduce Arena, a general evaluation platform
for multi-agent intelligence with 35 games of diverse logics and
representations. Furthermore, multi-agent intelligence is still at the stage
where many problems remain unexplored. Therefore, we provide a building toolkit
for researchers to easily invent and build novel multi-agent problems from the
provided game set based on a GUI-configurable social tree and five basic
multi-agent reward schemes. Finally, we provide Python implementations of five
state-of-the-art deep multi-agent reinforcement learning baselines. Along with
the baseline implementations, we release a set of 100 best agents/teams that we
can train with different training schemes for each game, as the base for
evaluating agents with population performance. As such, the research community
can perform comparisons under a stable and uniform standard. All the
implementations and accompanied tutorials have been open-sourced for the
community at https://sites.google.com/view/arena-unity/
Scalable Multiagent Coordination with Distributed Online Open Loop Planning
We propose distributed online open loop planning (DOOLP), a general framework
for online multiagent coordination and decision making under uncertainty. DOOLP
is based on online heuristic search in the space defined by a generative model
of the domain dynamics, which is exploited by agents to simulate and evaluate
the consequences of their potential choices.
We also propose distributed online Thompson sampling (DOTS) as an effective
instantiation of the DOOLP framework. DOTS models sequences of agent choices by
concatenating a number of multiarmed bandits for each agent and uses Thompson
sampling for dealing with action value uncertainty. The Bayesian approach
underlying Thompson sampling allows to effectively model and estimate
uncertainty about (a) own action values and (b) other agents' behavior. This
approach yields a principled and statistically sound solution to the
exploration-exploitation dilemma when exploring large search spaces with
limited resources.
We implemented DOTS in a smart factory case study with positive empirical
results. We observed effective, robust and scalable planning and coordination
capabilities even when only searching a fraction of the potential search space
Learning with Opponent-Learning Awareness
Multi-agent settings are quickly gathering importance in machine learning.
This includes a plethora of recent work on deep multi-agent reinforcement
learning, but also can be extended to hierarchical RL, generative adversarial
networks and decentralised optimisation. In all these settings the presence of
multiple learning agents renders the training problem non-stationary and often
leads to unstable training or undesired final results. We present Learning with
Opponent-Learning Awareness (LOLA), a method in which each agent shapes the
anticipated learning of the other agents in the environment. The LOLA learning
rule includes a term that accounts for the impact of one agent's policy on the
anticipated parameter update of the other agents. Results show that the
encounter of two LOLA agents leads to the emergence of tit-for-tat and
therefore cooperation in the iterated prisoners' dilemma, while independent
learning does not. In this domain, LOLA also receives higher payouts compared
to a naive learner, and is robust against exploitation by higher order
gradient-based methods. Applied to repeated matching pennies, LOLA agents
converge to the Nash equilibrium. In a round robin tournament we show that LOLA
agents successfully shape the learning of a range of multi-agent learning
algorithms from literature, resulting in the highest average returns on the
IPD. We also show that the LOLA update rule can be efficiently calculated using
an extension of the policy gradient estimator, making the method suitable for
model-free RL. The method thus scales to large parameter and input spaces and
nonlinear function approximators. We apply LOLA to a grid world task with an
embedded social dilemma using recurrent policies and opponent modelling. By
explicitly considering the learning of the other agent, LOLA agents learn to
cooperate out of self-interest. The code is at github.com/alshedivat/lola
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