Implementing Cst in Learning Layer of Csia for Higher Level of Intelligence

Development of cognitive architecture where the agents at different levels exhibit different levels of thinking. The paper primarily focus on building the skill tree at the learning layer of the architecture. These include the discovery of one’s own body, including its structure and dynamics. Also the acquisition of associated cognitive skills such as self and non-self-distinction. This can be achieved by implementing CST(Constructing Skill Trees). CST is a hierarchical reinforcement learning algorithm which can build skill trees from a set of sample solution trajectories obtained from demonstration. CST is much faster learning algorithm than skill chaining. CST can be applied to learning higher dimensional policies. Even unsuccessful episode can improve skills. Skills acquired using agent-centric features can be used for other problems.