11,073 research outputs found
Reinforcement Learning With Temporal Logic Rewards
Reinforcement learning (RL) depends critically on the choice of reward
functions used to capture the de- sired behavior and constraints of a robot.
Usually, these are handcrafted by a expert designer and represent heuristics
for relatively simple tasks. Real world applications typically involve more
complex tasks with rich temporal and logical structure. In this paper we take
advantage of the expressive power of temporal logic (TL) to specify complex
rules the robot should follow, and incorporate domain knowledge into learning.
We propose Truncated Linear Temporal Logic (TLTL) as specifications language,
that is arguably well suited for the robotics applications, together with
quantitative semantics, i.e., robustness degree. We propose a RL approach to
learn tasks expressed as TLTL formulae that uses their associated robustness
degree as reward functions, instead of the manually crafted heuristics trying
to capture the same specifications. We show in simulated trials that learning
is faster and policies obtained using the proposed approach outperform the ones
learned using heuristic rewards in terms of the robustness degree, i.e., how
well the tasks are satisfied. Furthermore, we demonstrate the proposed RL
approach in a toast-placing task learned by a Baxter robot
Learning Unmanned Aerial Vehicle Control for Autonomous Target Following
While deep reinforcement learning (RL) methods have achieved unprecedented
successes in a range of challenging problems, their applicability has been
mainly limited to simulation or game domains due to the high sample complexity
of the trial-and-error learning process. However, real-world robotic
applications often need a data-efficient learning process with safety-critical
constraints. In this paper, we consider the challenging problem of learning
unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire
a strategy that combines perception and control, we represent the policy by a
convolutional neural network. We develop a hierarchical approach that combines
a model-free policy gradient method with a conventional feedback
proportional-integral-derivative (PID) controller to enable stable learning
without catastrophic failure. The neural network is trained by a combination of
supervised learning from raw images and reinforcement learning from games of
self-play. We show that the proposed approach can learn a target following
policy in a simulator efficiently and the learned behavior can be successfully
transferred to the DJI quadrotor platform for real-world UAV control
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