1,409 research outputs found
On the robustness of learning in games with stochastically perturbed payoff observations
Motivated by the scarcity of accurate payoff feedback in practical
applications of game theory, we examine a class of learning dynamics where
players adjust their choices based on past payoff observations that are subject
to noise and random disturbances. First, in the single-player case
(corresponding to an agent trying to adapt to an arbitrarily changing
environment), we show that the stochastic dynamics under study lead to no
regret almost surely, irrespective of the noise level in the player's
observations. In the multi-player case, we find that dominated strategies
become extinct and we show that strict Nash equilibria are stochastically
stable and attracting; conversely, if a state is stable or attracting with
positive probability, then it is a Nash equilibrium. Finally, we provide an
averaging principle for 2-player games, and we show that in zero-sum games with
an interior equilibrium, time averages converge to Nash equilibrium for any
noise level.Comment: 36 pages, 4 figure
Deep Adversarial Reinforcement Learning With Noise Compensation by Autoencoder
We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this method, robust internal representation in a deep Q-network (DQN) was introduced by applying adversarial noise to disturb the DQN policy; however, it was compensated for by the autoencoder network. In particular, we proposed the use of a new type of adversarial noise: it encourages the policy to choose the worst action leading to the worst outcome at each state. When the proposed method, called deep Q-W-network regularized with an autoencoder (DQWAE), was applied to seven different games in an Atari 2600, the results were convincing. DQWAE exhibited greater robustness against the random/adversarial noise added to the input and accelerated the learning process more than the baseline DQN. When applied to a realistic automatic driving simulation, the proposed DRL method was found to be effective at rendering the acquired policy robust against random/adversarial noise
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games
In their seminal work, Nayyar et al. (2013) showed that imperfect information
can be abstracted away from common-payoff games by having players publicly
announce their policies as they play. This insight underpins sound solvers and
decision-time planning algorithms for common-payoff games. Unfortunately, a
naive application of the same insight to two-player zero-sum games fails
because Nash equilibria of the game with public policy announcements may not
correspond to Nash equilibria of the original game. As a consequence, existing
sound decision-time planning algorithms require complicated additional
mechanisms that have unappealing properties. The main contribution of this work
is showing that certain regularized equilibria do not possess the
aforementioned non-correspondence problem -- thus, computing them can be
treated as perfect information problems. Because these regularized equilibria
can be made arbitrarily close to Nash equilibria, our result opens the door to
a new perspective on solving two-player zero-sum games and, in particular,
yields a simplified framework for decision-time planning in two-player zero-sum
games, void of the unappealing properties that plague existing decision-time
planning approaches
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