Spelling suggestions: "subject:"robots reinforcement learning."" "subject:"robots einforcement learning.""
Q-learning for robot control /Gaskett, Chris. January 2002 (has links)
Thesis (Ph.D.)--Australian National University, 2002. / CD contains "Examples of continuous state and action Q-learning"
Self-organisation of internal models in autonomous robotsSmith Bize, Simon Cristobal January 2016 (has links)
Internal Models (IMs) play a significant role in autonomous robotics. They are mechanisms able to represent the input-output characteristics of the sensorimotor loop. In developmental robotics, open-ended learning of skills and knowledge serves the purpose of reaction to unexpected inputs, to explore the environment and to acquire new behaviours. The development of the robot includes self-exploration of the state-action space and learning of the environmental dynamics. In this dissertation, we explore the properties and benefits of the self-organisation of robot behaviour based on the homeokinetic learning paradigm. A homeokinetic robot explores the environment in a coherent way without prior knowledge of its configuration or the environment itself. First, we propose a novel approach to self-organisation of behaviour by artificial curiosity in the sensorimotor loop. Second, we study how different forward models settings alter the behaviour of both exploratory and goal-oriented robots. Diverse complexity, size and learning rules are compared to assess the importance in the robot’s exploratory behaviour. We define the self-organised behaviour performance in terms of simultaneous environment coverage and best prediction of future sensori inputs. Among the findings, we have encountered that models with a fast response and a minimisation of the prediction error by local gradients achieve the best performance. Third, we study how self-organisation of behaviour can be exploited to learn IMs for goal-oriented tasks. An IM acquires coherent self-organised behaviours that are then used to achieve high-level goals by reinforcement learning (RL). Our results demonstrate that learning of an inverse model in this context yields faster reward maximisation and a higher final reward. We show that an initial exploration of the environment in a goal-less yet coherent way improves learning. In the same context, we analyse the self-organisation of central pattern generators (CPG) by reward maximisation. Our results show that CPGs can learn favourable reward behaviour on high-dimensional robots using the self-organised interaction between degrees of freedom. Finally, we examine an on-line dual control architecture where we combine an Actor-Critic RL and the homeokinetic controller. With this configuration, the probing signal is generated by the exertion of the embodied robot experience with the environment. This set-up solves the problem of designing task-dependant probing signals by the emergence of intrinsically motivated comprehensible behaviour. Faster improvement of the reward signal compared to classic RL is achievable with this configuration.
Page generated in 0.149 seconds