114,958 research outputs found
Probabilistically Safe Policy Transfer
Although learning-based methods have great potential for robotics, one
concern is that a robot that updates its parameters might cause large amounts
of damage before it learns the optimal policy. We formalize the idea of safe
learning in a probabilistic sense by defining an optimization problem: we
desire to maximize the expected return while keeping the expected damage below
a given safety limit. We study this optimization for the case of a robot
manipulator with safety-based torque limits. We would like to ensure that the
damage constraint is maintained at every step of the optimization and not just
at convergence. To achieve this aim, we introduce a novel method which predicts
how modifying the torque limit, as well as how updating the policy parameters,
might affect the robot's safety. We show through a number of experiments that
our approach allows the robot to improve its performance while ensuring that
the expected damage constraint is not violated during the learning process
Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving
Tactical decision making for autonomous driving is challenging due to the
diversity of environments, the uncertainty in the sensor information, and the
complex interaction with other road users. This paper introduces a general
framework for tactical decision making, which combines the concepts of planning
and learning, in the form of Monte Carlo tree search and deep reinforcement
learning. The method is based on the AlphaGo Zero algorithm, which is extended
to a domain with a continuous state space where self-play cannot be used. The
framework is applied to two different highway driving cases in a simulated
environment and it is shown to perform better than a commonly used baseline
method. The strength of combining planning and learning is also illustrated by
a comparison to using the Monte Carlo tree search or the neural network policy
separately
Automated Speed and Lane Change Decision Making using Deep Reinforcement Learning
This paper introduces a method, based on deep reinforcement learning, for
automatically generating a general purpose decision making function. A Deep
Q-Network agent was trained in a simulated environment to handle speed and lane
change decisions for a truck-trailer combination. In a highway driving case, it
is shown that the method produced an agent that matched or surpassed the
performance of a commonly used reference model. To demonstrate the generality
of the method, the exact same algorithm was also tested by training it for an
overtaking case on a road with oncoming traffic. Furthermore, a novel way of
applying a convolutional neural network to high level input that represents
interchangeable objects is also introduced
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