4,142 research outputs found
Partner Selection for the Emergence of Cooperation in Multi-Agent Systems Using Reinforcement Learning
Social dilemmas have been widely studied to explain how humans are able to
cooperate in society. Considerable effort has been invested in designing
artificial agents for social dilemmas that incorporate explicit agent
motivations that are chosen to favor coordinated or cooperative responses. The
prevalence of this general approach points towards the importance of achieving
an understanding of both an agent's internal design and external environment
dynamics that facilitate cooperative behavior. In this paper, we investigate
how partner selection can promote cooperative behavior between agents who are
trained to maximize a purely selfish objective function. Our experiments reveal
that agents trained with this dynamic learn a strategy that retaliates against
defectors while promoting cooperation with other agents resulting in a
prosocial society.Comment:
Agent-Based Models and Human Subject Experiments
This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms
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
- …