22,834 research outputs found
Network formation by reinforcement learning: the long and medium run
We investigate a simple stochastic model of social network formation by the
process of reinforcement learning with discounting of the past. In the limit,
for any value of the discounting parameter, small, stable cliques are formed.
However, the time it takes to reach the limiting state in which cliques have
formed is very sensitive to the discounting parameter. Depending on this value,
the limiting result may or may not be a good predictor for realistic
observation times.Comment: 14 page
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
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
Truncating Temporal Differences: On the Efficient Implementation of TD(lambda) for Reinforcement Learning
Temporal difference (TD) methods constitute a class of methods for learning
predictions in multi-step prediction problems, parameterized by a recency
factor lambda. Currently the most important application of these methods is to
temporal credit assignment in reinforcement learning. Well known reinforcement
learning algorithms, such as AHC or Q-learning, may be viewed as instances of
TD learning. This paper examines the issues of the efficient and general
implementation of TD(lambda) for arbitrary lambda, for use with reinforcement
learning algorithms optimizing the discounted sum of rewards. The traditional
approach, based on eligibility traces, is argued to suffer from both
inefficiency and lack of generality. The TTD (Truncated Temporal Differences)
procedure is proposed as an alternative, that indeed only approximates
TD(lambda), but requires very little computation per action and can be used
with arbitrary function representation methods. The idea from which it is
derived is fairly simple and not new, but probably unexplored so far.
Encouraging experimental results are presented, suggesting that using lambda
> 0 with the TTD procedure allows one to obtain a significant learning
speedup at essentially the same cost as usual TD(0) learning.Comment: See http://www.jair.org/ for any accompanying file
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