2,101 research outputs found
Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches
This paper surveys the field of multiagent deep reinforcement learning. The
combination of deep neural networks with reinforcement learning has gained
increased traction in recent years and is slowly shifting the focus from
single-agent to multiagent environments. Dealing with multiple agents is
inherently more complex as (a) the future rewards depend on the joint actions
of multiple players and (b) the computational complexity of functions
increases. We present the most common multiagent problem representations and
their main challenges, and identify five research areas that address one or
more of these challenges: centralised training and decentralised execution,
opponent modelling, communication, efficient coordination, and reward shaping.
We find that many computational studies rely on unrealistic assumptions or are
not generalisable to other settings; they struggle to overcome the curse of
dimensionality or nonstationarity. Approaches from psychology and sociology
capture promising relevant behaviours such as communication and coordination.
We suggest that, for multiagent reinforcement learning to be successful, future
research addresses these challenges with an interdisciplinary approach to open
up new possibilities for more human-oriented solutions in multiagent
reinforcement learning.Comment: 37 pages, 6 figure
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics
obtained by a population of intelligence agents, driven by million-agent
reinforcement learning. Our intention is to put intelligent agents into a
simulated natural context and verify if the principles developed in the real
world could also be used in understanding an artificially-created intelligent
population. To achieve this, we simulate a large-scale predator-prey world,
where the laws of the world are designed by only the findings or logical
equivalence that have been discovered in nature. We endow the agents with the
intelligence based on deep reinforcement learning (DRL). In order to scale the
population size up to millions agents, a large-scale DRL training platform with
redesigned experience buffer is proposed. Our results show that the population
dynamics of AI agents, driven only by each agent's individual self-interest,
reveals an ordered pattern that is similar to the Lotka-Volterra model studied
in population biology. We further discover the emergent behaviors of collective
adaptations in studying how the agents' grouping behaviors will change with the
environmental resources. Both of the two findings could be explained by the
self-organization theory in nature.Comment: Full version of the paper presented at AAMAS 2018 (International
Conference on Autonomous Agents and Multiagent Systems
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