1,748 research outputs found
Teaching Agents with Deep Apprenticeship Learning
As the field of robotic and humanoid systems expand, more research is being done on how to best control systems to perform complex, smart tasks. Many supervised learning and classification techniques require large datasets, and only result in the system mimicking what it was given. The sequential relationship within datasets used for task learning results in Markov decision problems that traditional classification algorithms cannot solve. Reinforcement learning helps to solve these types of problems using a reward/punishment and exploration/exploitation methodology without the need for datasets. While this works for simple systems, complex systems are more difficult to teach using traditional reinforcement learning. Often these systems have complex, non-linear, non-intuitive cost functions which make it near impossible to model. Inverse reinforcement learning, or apprenticeship learning algorithms, learn complex cost functions based on input from an expert system. Deep learning has also made a large impact in learning complex systems, and has achieved state of the art results in several applications. Using methods from apprenticeship learning and deep learning a system can be taught complex tasks from watching an expert. It is shown here how well these types of networks solve a specific task, and how well they generalize and understand the task through raw pixel data from an expert
Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation
Imitation learning is an effective approach for autonomous systems to acquire
control policies when an explicit reward function is unavailable, using
supervision provided as demonstrations from an expert, typically a human
operator. However, standard imitation learning methods assume that the agent
receives examples of observation-action tuples that could be provided, for
instance, to a supervised learning algorithm. This stands in contrast to how
humans and animals imitate: we observe another person performing some behavior
and then figure out which actions will realize that behavior, compensating for
changes in viewpoint, surroundings, object positions and types, and other
factors. We term this kind of imitation learning "imitation-from-observation,"
and propose an imitation learning method based on video prediction with context
translation and deep reinforcement learning. This lifts the assumption in
imitation learning that the demonstration should consist of observations in the
same environment configuration, and enables a variety of interesting
applications, including learning robotic skills that involve tool use simply by
observing videos of human tool use. Our experimental results show the
effectiveness of our approach in learning a wide range of real-world robotic
tasks modeled after common household chores from videos of a human
demonstrator, including sweeping, ladling almonds, pushing objects as well as a
number of tasks in simulation.Comment: Accepted at ICRA 2018, Brisbane. YuXuan Liu and Abhishek Gupta had
equal contributio
Eligibility Propagation to Speed up Time Hopping for Reinforcement Learning
A mechanism called Eligibility Propagation is proposed to speed up the Time
Hopping technique used for faster Reinforcement Learning in simulations.
Eligibility Propagation provides for Time Hopping similar abilities to what
eligibility traces provide for conventional Reinforcement Learning. It
propagates values from one state to all of its temporal predecessors using a
state transitions graph. Experiments on a simulated biped crawling robot
confirm that Eligibility Propagation accelerates the learning process more than
3 times.Comment: 7 page
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