23,053 research outputs found
Discovering Blind Spots in Reinforcement Learning
Agents trained in simulation may make errors in the real world due to
mismatches between training and execution environments. These mistakes can be
dangerous and difficult to discover because the agent cannot predict them a
priori. We propose using oracle feedback to learn a predictive model of these
blind spots to reduce costly errors in real-world applications. We focus on
blind spots in reinforcement learning (RL) that occur due to incomplete state
representation: The agent does not have the appropriate features to represent
the true state of the world and thus cannot distinguish among numerous states.
We formalize the problem of discovering blind spots in RL as a noisy supervised
learning problem with class imbalance. We learn models to predict blind spots
in unseen regions of the state space by combining techniques for label
aggregation, calibration, and supervised learning. The models take into
consideration noise emerging from different forms of oracle feedback, including
demonstrations and corrections. We evaluate our approach on two domains and
show that it achieves higher predictive performance than baseline methods, and
that the learned model can be used to selectively query an oracle at execution
time to prevent errors. We also empirically analyze the biases of various
feedback types and how they influence the discovery of blind spots.Comment: To appear at AAMAS 201
Certified Reinforcement Learning with Logic Guidance
This paper proposes the first model-free Reinforcement Learning (RL)
framework to synthesise policies for unknown, and continuous-state Markov
Decision Processes (MDPs), such that a given linear temporal property is
satisfied. We convert the given property into a Limit Deterministic Buchi
Automaton (LDBA), namely a finite-state machine expressing the property.
Exploiting the structure of the LDBA, we shape a synchronous reward function
on-the-fly, so that an RL algorithm can synthesise a policy resulting in traces
that probabilistically satisfy the linear temporal property. This probability
(certificate) is also calculated in parallel with policy learning when the
state space of the MDP is finite: as such, the RL algorithm produces a policy
that is certified with respect to the property. Under the assumption of finite
state space, theoretical guarantees are provided on the convergence of the RL
algorithm to an optimal policy, maximising the above probability. We also show
that our method produces ''best available'' control policies when the logical
property cannot be satisfied. In the general case of a continuous state space,
we propose a neural network architecture for RL and we empirically show that
the algorithm finds satisfying policies, if there exist such policies. The
performance of the proposed framework is evaluated via a set of numerical
examples and benchmarks, where we observe an improvement of one order of
magnitude in the number of iterations required for the policy synthesis,
compared to existing approaches whenever available.Comment: This article draws from arXiv:1801.08099, arXiv:1809.0782
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
- …