Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
نویسندگان
چکیده
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.
منابع مشابه
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
Synthesizing physiologically-accurate human movement in a variety of conditions can help practitioners plan surgeries, design experiments, or prototype assistive devices in simulated environments, reducing time and costs and improving treatment outcomes. Because of the large and complex solution spaces of biomechanical models, current methods are constrained to specific movements and models, re...
متن کاملDesigning Learning Spaces for Children with Autism Spectrum Disorder
Although the problems and disabilities caused by autism spectrum disorders are constant companions to these individuals, timely treatment interventions can provide the necessary grounds for their empowerment., However, one thing that deserves attention is that regular learning environments are not often designed to meet the needs and moods of children with autism spectrum disorder. Likewise, a...
متن کاملDynamic Obstacle Avoidance by Distributed Algorithm based on Reinforcement Learning (RESEARCH NOTE)
In this paper we focus on the application of reinforcement learning to obstacle avoidance in dynamic Environments in wireless sensor networks. A distributed algorithm based on reinforcement learning is developed for sensor networks to guide mobile robot through the dynamic obstacles. The sensor network models the danger of the area under coverage as obstacles, and has the property of adoption o...
متن کاملWeb pages ranking algorithm based on reinforcement learning and user feedback
The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...
متن کاملLightweight Adaptation in Model-Based Reinforcement Learning
Reinforcement learning algorithms can train an agent to operate successfully in a stationary environment. Most real-world environments, however, are subject to change over time. Research in the areas of transfer learning and lifelong learning addresses this problem by developing new algorithms that allow agents to adapt to environment change. Current trends in this area include model-free learn...
متن کامل