Sequence alignment is an important operation in computational biology. Both dynamic programming and A* heuristic search algorithms for optimal sequence alignment are discussed and evaluated. Presented here are two new algorithms for optimal pairwise sequence alignment which outperform traditional methods on very large problem instances (hundreds of thousands of characters, for example). The tec...

Heuristic Dynamic Programming (HDP) is the simplest kind of Adaptive Critic which is a powerful form of reinforcement control 1]. It can be used to maximize or minimize any utility function, such as total energy or trajectory error, of a system over time in a noisy environment. Unlike supervised learning, adaptive critic design does not require the desired control signals be known. Instead, fee...

In this paper, we analyze an internal goal structure based on heuristic dynamic programming, named GrHDP, to tackle the 2-D maze navigation problem. Classical reinforcement learning approaches have been introduced to solve this problem in literature, yet no intermediate reward has been assigned before reaching the final goal. In this paper, we integrated one additional network, namely goal netw...

The rollout algorithm is a suboptimal control method for deterministic and stochastic problems that can be solved by dynamic programming. In this short note, we derive an extension of the rollout algorithm that applies to constrained deterministic dynamic programming problems, and relies on a suboptimal policy, called base heuristic. Under suitable assumptions, we show that if the base heuristi...

The rollout algorithm is a suboptimal control method for deterministic and stochastic problems that can be solved by dynamic programming. In this short note, we derive an extension of the rollout algorithm that applies to constrained deterministic dynamic programming problems, and relies on a suboptimal policy, called base heuristic. Under suitable assumptions, we show that if the base heuristi...

We discuss a variety of adaptive critic designs (ACD’s) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins leads to an explanation of three design families: Heuristic dynamic programming (HDP), dual heu...

This paper investigates an adaptive dynamic programming (ADP)-based energy management control strategy for a series-parallel hybrid electric vehicle (HEV). can further minimize the equivalent fuel consumption while satisfying battery level constraints and power demand. Dual heuristic (DHP) is one of basic structures ADP, combining reinforcement learning, (DP) optimization principle, neural netw...