24 research outputs found
Lifelong Reinforcement Learning On Mobile Robots
Machine learning has shown tremendous growth in the past decades, unlocking new capabilities in a variety of fields including computer vision, natural language processing, and robotic control. While the sophistication of individual problems a learning system can handle has greatly advanced, the ability of a system to extend beyond an individual problem to adapt and solve new problems has progressed more slowly. This thesis explores the problem of progressive learning. The goal is to develop methodologies that accumulate, transfer, and adapt knowledge in applied settings where the system is faced with the ambiguity and resource limitations of operating in the physical world.
There are undoubtedly many challenges to designing such a system, my thesis looks at the component of this problem related to how knowledge from previous tasks can be a benefit in the domain of reinforcement learning where the agent receives rewards for positive actions. Reinforcement learning is particularly difficult when training on physical systems, like mobile robots, where repeated trials can
damage the system and unrestricted exploration is often associated with safety risks. I investigate how knowledge can be efficiently accumulated and applied to future reinforcement learning problems on mobile robots in order to reduce sample complexity and enable systems to adapt to novel settings. Doing this involves mathematical models which can combine knowledge from multiple tasks, methods for restructuring optimizations and data collection to handle sequential updates, and data selection strategies that can be used to address resource limitations
Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning
Providing an efficient strategy to navigate safely through unsignaled
intersections is a difficult task that requires determining the intent of other
drivers. We explore the effectiveness of Deep Reinforcement Learning to handle
intersection problems. Using recent advances in Deep RL, we are able to learn
policies that surpass the performance of a commonly-used heuristic approach in
several metrics including task completion time and goal success rate and have
limited ability to generalize. We then explore a system's ability to learn
active sensing behaviors to enable navigating safely in the case of occlusions.
Our analysis, provides insight into the intersection handling problem, the
solutions learned by the network point out several shortcomings of current
rule-based methods, and the failures of our current deep reinforcement learning
system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018
Game Theoretic Decision Making by Actively Learning Human Intentions Applied on Autonomous Driving
The ability to estimate human intentions and interact with human drivers
intelligently is crucial for autonomous vehicles to successfully achieve their
objectives. In this paper, we propose a game theoretic planning algorithm that
models human opponents with an iterative reasoning framework and estimates
human latent cognitive states through probabilistic inference and active
learning. By modeling the interaction as a partially observable Markov decision
process with adaptive state and action spaces, our algorithm is able to
accomplish real-time lane changing tasks in a realistic driving simulator. We
compare our algorithm's lane changing performance in dense traffic with a
state-of-the-art autonomous lane changing algorithm to show the advantage of
iterative reasoning and active learning in terms of avoiding overly
conservative behaviors and achieving the driving objective successfully