Our goal is to develop a hybrid cognitive model of how humans acquire skills on complex cognitive tasks. We are pursuing this goal by designing hybrid computational architectures for the NRL Navigation task, which requires competent sensorimotor coordination. In this paper, we empirically compare two methods for control knowledge acquisition (reinforcement learning and a novel variant of action...

Many traditional reinforcement-learning algorithms have been designed for problems with small finite state and action spaces. Learning in such discrete problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action spaces, which can make learning a good decision policy even more involved. In this chapter we discuss how to ...

In this review, we summarized Monte Carlo Bayesian Reinforcement learning, and explored the possible improvements by proposing better sampling strategy by adaptive sampling. Monte Carlo Bayesian Reinforcement learning is a simple and general approach which samples a finite set of hypotheses from the model parameter space. MC-BRL generates a discrete POMDP that approximates the underlying BRL pr...

Reinforcement learning (RL) is a powerful machine-learning methodology that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. There has been considerable work on applying RL, a method originally conceived for discrete state-action spaces, to problems with continuous states. The extension of RL to allow continuous actions, on the other h...

It is difficult to apply traditional reinforcement learning algorithms to robots, due to problems with large and continuous domains, partial observability, and limited numbers of learning experiences. This paper deals with these problems by combining: 1. reinforcement learning with memory, implemented using an LSTM recurrent neural network whose inputs are discrete events extracted from raw inp...

|Associative reinforcement learning (ARL) tasks de ned originally by Barto and Anandan [1] combine elements of problems involving optimization under uncertainty, studied by learning automata theorists, and supervised learning pattern-classi cation. The stochastic real-valued (SRV) unit algorithm [6] has been designed for an extended version of ARL tasks wherein the learning system's outputs can...

We present several new algorithms for multiagent reinforcement learning. A common feature of these algorithms is a parameterized, structured representation of a policy or value function. This structure is leveraged in an approach we call coordinated reinforcement learning, by which agents coordinate both their action selection activities and their parameter updates. Within the limits of our par...

Learning automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms and are able to control the stochastic games. In this paper, the concepts of stigmergy and entropy are imported into learning automata based multi-agent systems with the purpose of providing a simple framework for interaction and coordination in multi-agent systems and spe...

The learning automata operate in unknown random environments and progressively improve their performance via a learning process. The learning automata are very useful for optimization of multi-modal functions when the function is unknown and only noise-corrupted evaluations are available. In this paper we propose a new hybrid algorithm for noisy optimization. This model is obtained by combining...