Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
FC Portugal Team Description: RoboCup 2000 Simulation League Champion
RoboCup 2000: Robot Soccer World Cup IV
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
A 3D Simulator of Multiple Legged Robots Based on USARSim
RoboCup 2006: Robot Soccer World Cup X
| Hi-index | 0.00 |
One of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-Ihumanoids robots to be simulated under USARSimenvironment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robot's performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots.