No abstract

Conventional reinforcement learning methods for Markov decision processes rely on weakly-guided, stochastic searches to drive the learning process. It can therefore be difficult to predict what agent behaviors might emerge. In this paper, we consider an information-theoretic cost function for performing constrained stochastic searches that promote the formation of risk-averse to risk-favoring behaviors. This cost function is the value of information, which provides the optimal trade-off between the expected return of a policy and the policy's complexity; policy complexity is measured by number of bits and controlled by a single hyperparameter on the cost function. As the policy complexity is reduced, the agents will increasingly eschew risky actions. This reduces the potential for high accrued rewards. As the policy complexity increases, the agents will take actions, regardless of the risk, that can raise the long-term rewards. The obtainable reward depends on a single, tunable hyperparameter that regulates the degree of policy complexity.We evaluate the performance of value-of-information-based policies on a stochastic version of Ms. Pac-Man. A major component of this paper is the demonstration that ranges of policy complexity values yield different game-play styles and explaining why this occurs. We also show that our reinforcementlearning search mechanism is more efficient than the others we utilize. This result implies that the value of information theory is appropriate for framing the exploitation-exploration trade-off in reinforcement learning.Index Terms-Value of information, constrained search, reinforcement learning, information theory Isaac J. Sledge is with the 2 objective of the agent is to clear the environment of pellets while navigating around the ghosts. However, after activating certain power-ups, the ghosts become vulnerable for a brief period of time. The agent can consume these ghosts for a score boost.The switch in ghost dynamics necessitates a change in the game-play strategy, since multiple distinct modes of behavior are required under different conditions [4,5]. Despite the need for multi-modal behaviors, conventional reinforcement-learning approaches have focused on constructing monolithic policies. Such policies would implement the same agent behaviors regardless of the vulnerability of the ghosts. Although it is possible to represent multimodal behavior with these policies, it can be difficult to learn such behavior. This is, in part, due to risk. For instance, throughout the learning process, an agent may have learned to avoid colliding with the ghosts. Without straying from this behavior, the agent will not learn that there are instances where it can safely chase the ghosts.In this paper, we consider an information-theoretic learning [6] approach for performing constrained stochastic searches that promote a continuum of risk-averse to risk-favoring agent behaviors during reinforcement learning. This, in turn, leads to a principled exploration of the state-action space that aids in the...

No abstract

No abstract

All clustering algorithms ultimately rely on one or more human inputs, and the most important input is number of clusters (c) to seek. There are "adaptive" methods which claim to relieve the user from making this most important choice, but these methods ultimately make the choice by thresholding some value in the code. Thus, the choice of c is transferred to the equivalent choice of the hidden threshold that determines c "automatically". This work investigates a new technique for estimating the number of clusters to look for in unlabeled data utilizing the VAT [Visual Assessment of Cluster Tendency] algorithm, coupled with a judicious combination of several common image processing techniques. Our method is also at the mercy of several parametric choices, but these parameters are pretty gentle, and hold up well in the examples we have run so far. Several numerical examples are presented to illustrate the effectiveness of the proposed method.