Search results for "Reinforcement learning algorithm"
showing 3 items of 3 documents
A comparison between a two feedback control loop and a reinforcement learning algorithm for compliant low-cost series elastic actuators
2020
Highly-compliant elastic actuators have become progressively prominent over the last years for a variety of robotic applications. With remarkable shock tolerance, elastic actuators are appropriate for robots operating in unstructured environments. In accordance with this trend, a novel elastic actuator was recently designed by our research group for Serpens, a low-cost, open-source and highly-compliant multi-purpose modular snake robot. To control the newly designed elastic actuators of Serpens, a two-feedback loops position control algorithm was proposed. The inner controller loop is implemented as a model reference adaptive controller (MRAC), while the outer control loop adopts a fuzzy pr…
Robust Adaptive Modulation and Coding (AMC) selection in LTE systems using reinforcement learning
2014
Adaptive Modulation and Coding (AMC) in LTE networks is commonly employed to improve system throughput by ensuring more reliable transmissions. Most of existing AMC methods select the modulation and coding scheme (MCS) using pre-computed mappings between MCS indexes and channel quality indicator (CQI) feedbacks that are periodically sent by the receivers. However, the effectiveness of this approach heavily depends on the assumed channel model. In addition CQI feedback delays may cause throughput losses. In this paper we design a new AMC scheme that exploits a reinforcement learning algorithm to adjust at run-time the MCS selection rules based on the knowledge of the effect of previous AMC d…
Road Detection for Reinforcement Learning Based Autonomous Car
2020
Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous.In this paper, we propose a new Road Detection algorithm using online supervised learning based on a Neural Network architecture. This algorithm is designed to support a Reinforcement Learning algorithm (for example, the standard Proximal Policy Optimization or PPO) by detecting when the car is in an adverse condition. Specifically, the PPO gets a penalty whenever the virtual automobile gets stuck or drives off the road with any of its four wheels.Initial experiments …