Reinforcement Learning addresses the problem of learning to select actions in unknown environments. Due to the poor performance of Reinforcement Learning in more complex and thus more realistic tasks with large state spaces and sparse reinforcement, much effort is done to speed up learning as well as on finding structure in problem spaces [11, 12]. Models are introduced in order to improve lear...

To simulate dynamic soft geo-objects and reflect geoscientific laws, the authors discuss the characteristics, mathematical expression, parameter representation and rendering of GIS Flow Element (FE) and GIS Soft Voxel (SV). The simulation and rendering algorithms of GIS FE and GIS SV are based on particle system and metaball technology in computer graphics. The main differences are that GIS FE ...

Hippocampal place fields have been shown to reflect behaviorally relevant aspects of space. For instance, place fields tend to be skewed along commonly traveled directions, they cluster around rewarded locations, and they are constrained by the geometric structure of the environment. We hypothesize a set of design principles for the hippocampal cognitive map that explain how place fields repres...

Abstract This paper presents a robust approach using artificial neural networks in the form of a Self Organizing Map (SOM) as a semi-automatic method for analysis and identification of morphometric features in two completely different environments, the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) in a complex mountainous humid area and Yardangs in Lut Desert, Iran, a hyper...

abstract this paper presents a robust approach using artificial neural networks in the form of a self organizing map (som) as a semi-automatic method for analysis and identification of morphometric features in two completely different environments, the man and biosphere reserve “eastern carpathians” (central europe) in a complex mountainous humid area and yardangs in lut desert, iran, a hyper a...

Grid computing utilizes distributed heterogeneous resources to support large-scale or complicated computing tasks, and an appropriate resource scheduling algorithm is fundamentally important for the success of Grid applications. Due to the complex and dynamic properties of Grid environments, traditional model-basedmethodsmay result in poor scheduling performance in practice. Scalability and ada...

Reinforcement learning methods for discrete and semi-Markov decision problems such as Real-Time Dynamic Programming can be generalized for Controlled Diiusion Processes. The optimal control problem reduces to a boundary value problem for a fully nonlinear second-order elliptic diierential equation of Hamilton-Jacobi-Bellman (HJB-) type. Numerical analysis provides multi-grid methods for this ki...

The problem of autonomous vehicle navigation between lanes, around obstacles and towards a short term goal can be solved using Reinforcement Learning. The multi-lane road ahead of a vehicle may be represented by a Markov Decision Process (MDP) grid-world containing positive and negative rewards, allowing for practical computation of an optimal path using either value iteration (VI) or policy it...

We propose a modular reinforcement learning algorithm which decomposes a Markov decision process into independent modules. Each module is trained using Sarsa(λ). We introduce three algorithms for forming global policy from modules policies, and demonstrate our results using a 2D grid world.

Reinforcement learning addresses the problem of learning to select actions in order to maximize one’s performance in unknown environments. To scale reinforcement learning to complex real-world tasks, such as typically studied in AI, one must ultimately be able to discover the structure in the world, in order to abstract away the myriad of details and to operate in more tractable problem spaces....