Understanding Model-Based Reinforcement Learning and its Application in Safe Reinforcement Learning

Model-based reinforcement learning algorithms have been shown to achieve successful results on various continuous control benchmarks, but the understanding of model-based methods is limited. We try to interpret how model-based method works through novel experiments on state-of-the-art algorithms with an emphasis on the model learning part. We evaluate the role of the model learning in policy optimization and propose methods to learn a more accurate model. With a better understanding of model-based reinforcement learning, we then apply model-based methods to solve safe reinforcement learning (RL) problems with near-zero violation of hard constraints throughout training. Drawing an analogy with how humans and animals learn to perform safe actions, we break down the safe RL problem into three stages. First, we train agents in a constraint-free environment to learn a performant policy for reaching high rewards, and simultaneously learn a model of the dynamics. Second, we use model-based methods to plan safe actions and train a safeguarding policy from these actions through imitation. Finally, we propose a factored framework to train an overall policy that mixes the performant policy and the safeguarding policy. This three-step curriculum ensures near-zero violation of safety constraints at all times. As an advantage of model-based method, the sample complexity required at the second and third steps of the process is significantly lower than model-free methods and can enable online safe learning. We demonstrate the effectiveness of our methods in various continuous control problems and analyze the advantages over state-of-the-art approaches

Holding safely : guidance for residential child care practitioners and managers about physically restraining children and young people

Residential child care is intensive and at times very diffificult work. Staff in residential childcare, therefore, need training, advice, supervision and support in undertaking this demanding work, since they are often doing the hardest of social care jobs. This good practice guidance has been commissioned to assist practitioners in working out policies and practices for restraining children and young people where no other appropriate options are available

Learning Feedback Terms for Reactive Planning and Control

With the advancement of robotics, machine learning, and machine perception, increasingly more robots will enter human environments to assist with daily tasks. However, dynamically-changing human environments requires reactive motion plans. Reactivity can be accomplished through replanning, e.g. model-predictive control, or through a reactive feedback policy that modifies on-going behavior in response to sensory events. In this paper, we investigate how to use machine learning to add reactivity to a previously learned nominal skilled behavior. We approach this by learning a reactive modification term for movement plans represented by nonlinear differential equations. In particular, we use dynamic movement primitives (DMPs) to represent a skill and a neural network to learn a reactive policy from human demonstrations. We use the well explored domain of obstacle avoidance for robot manipulation as a test bed. Our approach demonstrates how a neural network can be combined with physical insights to ensure robust behavior across different obstacle settings and movement durations. Evaluations on an anthropomorphic robotic system demonstrate the effectiveness of our work.Comment: 8 pages, accepted to be published at ICRA 2017 conferenc

Child–parent interaction in relation to road safety education : Part 2 – main report

• Children and young people are particularly vulnerable road users. • Child pedestrian injury rates are poor compared with the rest of Europe. • The factors that impact on children’s road safety and their capability in traffic are numerous, multi-faceted and complex. • • The systematic review conducted by Cattan et al. (2008) as the initial phase of this study shows that: • parents see themselves as being responsible for developing their children’s road safety awareness and skills; • holding hands is the most common road-crossing interaction between parents and children; • adults rarely make use of road-crossing events to give oral instructions; • few parents and children are consistent in their road-crossing behaviour; • roadside training by volunteer parents for groups of children can lead to significant improvements in children’s road safety behaviour; • belief in fate seems to influence the likelihood of parents using restraints, such as seat belts or car seats, with their children; and • parents’ understanding of the child’s perspective in carrying out road safety tasks and their motivation to actively involve their child in making decisions at the roadside can be improved through training. • Social Cognitive Theory (Bandura, 1986) suggests that the modelling role of parents can make a significant contribution to children’s learning about road use and their development of traffic competence whether or not parents are aware of this. • The main aim of this study was to explore the way parents influence children and young people aged 0–16 years to be safer road users. • This study included children and young people aged 5–16 and parents of children aged 0–16 years old

VIRTUAL ROBOT LOCOMOTION ON VARIABLE TERRAIN WITH ADVERSARIAL REINFORCEMENT LEARNING

Reinforcement Learning (RL) is a machine learning technique where an agent learns to perform a complex action by going through a repeated process of trial and error to maximize a well-defined reward function. This form of learning has found applications in robot locomotion where it has been used to teach robots to traverse complex terrain. While RL algorithms may work well in training robot locomotion, they tend to not generalize well when the agent is brought into an environment that it has never encountered before. Possible solutions from the literature include training a destabilizing adversary alongside the locomotive learning agent. The destabilizing adversary aims to destabilize the agent by applying external forces to it, which may help the locomotive agent learn to deal with unexpected scenarios. For this project, we will train a robust, simulated quadruped robot to traverse a variable terrain. We compare and analyze Proximal Policy Optimization (PPO) with and without the use of an adversarial agent, and determine which use of PPO produces the best results

Guidance on minimising the use of physical restraint in Scotland’s residential child care establishments

This guidance has been developed by a working group drawn from a comprehensive group of stakeholders representing all sectors, including the regulatory and inspection agencies and advocacy services. The main task of the group was to, building on the guidance in Holding Safely, clarify procedures for staff, service users and regulators, and help staff to understand when it is safe and appropriate to restrain a child

New ways of working in acute inpatient care: a case for change

This position paper focuses on the current tensions and challenges of aligning inpatient care with innovations in mental health services. It argues that a cultural shift is required within inpatient services. Obstacles to change including traditional perceptions of the role and responsibilities of the psychiatrist are discussed. The paper urges all staff working in acute care to reflect on the service that they provide, and to consider how the adoption of new ways of working might revolutionise the organisational culture. This cultural shift offers inpatient staff the opportunity to fully utilise their expertise. New ways of working may be perceived as a threat to existing roles and responsibilities or as an exciting opportunity for professional development with increased job satisfaction. Above all, the move to new ways of working, which is gathering pace throughout the UK, could offer service users1 a quality of care that meets their needs and expectations