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In this paper, a reinforced learning method for biped walking is proposed, where the robot learns to appropriately modulate an observed walking pattern. The biped robot was equipped with two Q-learning mechanisms. First, the robot learns a policy to adjust a defective walking pattern, gait-by-gait, into a more stable one. To avoid the complexity of adjusting too many joints of a humanoid robot and to speed up the learning process, the dimensionality of the action space was reduced. In turn, the other learning mechanism trained the robot to walk in a refined pattern, allowing it to walk faster without the loss of other required criteria, such as walking straight. This approach was implemented with both a simulated robot model and an actual biped robot. The results from the simulations and experiments show that successful walking policies were obtained. The learning system works quickly enough so that the robot was able to continually adapt to the terrain as it walked.

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Education is foundation of any modern society. Self Discovery is a key to an individual's education success. Self Discovery learning is the ability of an individual to learn through personal experience and natural exploration. Online Discovery learning tends to be learner-driven and learner-initiated. Digital repository on web is supportive for online discovery learning. In recent era, availability of infrastructure, flexibility of time, learning resources and their means of sharing has increased adaptability of World Wide Web to learn and attain knowledge to a great extent. However, most of the e-learners find difficulty to select location specific, relevant and significant tutorial on the web. Search engines results pages are not based on the interest, knowledge level and literacy rate of learner. More amount of time spent on searching and filtering and comparatively less time is devoted in actual learning. This paper proposes an agent based personalized intelligent e-learning model. The essence of perso-nalized search engine is to save learner's time to re-rank future result list. This Model uses multiple agents which delivers per-sonalized SERP (Search Engine Results Page) and is more suited for personalization of web pages based on learner's query expanded to manifold queries with novel concept of keyword research and discover knowledge using browser's behaviour. The proposed model uses web usage mining techniques to discover the knowledge of learner.

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