17 research outputs found
Single Bit and Reduced Dimension Diffusion Strategies Over Distributed Networks
We introduce novel diffusion based adaptive estimation strategies for
distributed networks that have significantly less communication load and
achieve comparable performance to the full information exchange configurations.
After local estimates of the desired data is produced in each node, a single
bit of information (or a reduced dimensional data vector) is generated using
certain random projections of the local estimates. This newly generated data is
diffused and then used in neighboring nodes to recover the original full
information. We provide the complete state-space description and the mean
stability analysis of our algorithms.Comment: Submitted to the IEEE Signal Processing Letter
Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load
We study the compressive diffusion strategies over distributed networks based
on the diffusion implementation and adaptive extraction of the information from
the compressed diffusion data. We demonstrate that one can achieve a comparable
performance with the full information exchange configurations, even if the
diffused information is compressed into a scalar or a single bit. To this end,
we provide a complete performance analysis for the compressive diffusion
strategies. We analyze the transient, steady-state and tracking performance of
the configurations in which the diffused data is compressed into a scalar or a
single-bit. We propose a new adaptive combination method improving the
convergence performance of the compressive diffusion strategies further. In the
new method, we introduce one more freedom-of-dimension in the combination
matrix and adapt it by using the conventional mixture approach in order to
enhance the convergence performance for any possible combination rule used for
the full diffusion configuration. We demonstrate that our theoretical analysis
closely follow the ensemble averaged results in our simulations. We provide
numerical examples showing the improved convergence performance with the new
adaptive combination method.Comment: Submitted to IEEE Transactions on Signal Processin
On the Global Convergence of Stochastic Fictitious Play in Stochastic Games with Turn-based Controllers
This paper presents a learning dynamic with almost sure convergence guarantee
for any stochastic game with turn-based controllers (on state transitions) as
long as stage-payoffs have stochastic fictitious-play-property. For example,
two-player zero-sum and n-player potential strategic-form games have this
property. Note also that stage-payoffs for different states can have different
structures such as they can sum to zero in some states and be identical in
others. The dynamics presented combines the classical stochastic fictitious
play with value iteration for stochastic games. There are two key properties:
(i) players play finite horizon stochastic games with increasing lengths within
the underlying infinite-horizon stochastic game, and (ii) the turn-based
controllers ensure that the auxiliary stage-games (induced from the
continuation payoff estimated) have the stochastic fictitious-play-property
A Novel Family of Adaptive Filtering Algorithms Based on The Logarithmic Cost
We introduce a novel family of adaptive filtering algorithms based on a
relative logarithmic cost. The new family intrinsically combines the higher and
lower order measures of the error into a single continuous update based on the
error amount. We introduce important members of this family of algorithms such
as the least mean logarithmic square (LMLS) and least logarithmic absolute
difference (LLAD) algorithms that improve the convergence performance of the
conventional algorithms. However, our approach and analysis are generic such
that they cover other well-known cost functions as described in the paper. The
LMLS algorithm achieves comparable convergence performance with the least mean
fourth (LMF) algorithm and extends the stability bound on the step size. The
LLAD and least mean square (LMS) algorithms demonstrate similar convergence
performance in impulse-free noise environments while the LLAD algorithm is
robust against impulsive interferences and outperforms the sign algorithm (SA).
We analyze the transient, steady state and tracking performance of the
introduced algorithms and demonstrate the match of the theoretical analyzes and
simulation results. We show the extended stability bound of the LMLS algorithm
and analyze the robustness of the LLAD algorithm against impulsive
interferences. Finally, we demonstrate the performance of our algorithms in
different scenarios through numerical examples.Comment: Submitted to IEEE Transactions on Signal Processin
Episodic Logit-Q Dynamics for Efficient Learning in Stochastic Teams
We present new learning dynamics combining (independent) log-linear learning
and value iteration for stochastic games within the auxiliary stage game
framework. The dynamics presented provably attain the efficient equilibrium
(also known as optimal equilibrium) in identical-interest stochastic games,
beyond the recent concentration of progress on provable convergence to some
(possibly inefficient) equilibrium. The dynamics are also independent in the
sense that agents take actions consistent with their local viewpoint to a
reasonable extent rather than seeking equilibrium. These aspects can be of
practical interest in the control applications of intelligent and autonomous
systems. The key challenges are the convergence to an inefficient equilibrium
and the non-stationarity of the environment from a single agent's viewpoint due
to the adaptation of others. The log-linear update plays an important role in
addressing the former. We address the latter through the play-in-episodes
scheme in which the agents update their Q-function estimates only at the end of
the episodes