17,239 research outputs found
Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
AI systems are increasingly applied to complex tasks that involve interaction
with humans. During training, such systems are potentially dangerous, as they
haven't yet learned to avoid actions that could cause serious harm. How can an
AI system explore and learn without making a single mistake that harms humans
or otherwise causes serious damage? For model-free reinforcement learning,
having a human "in the loop" and ready to intervene is currently the only way
to prevent all catastrophes. We formalize human intervention for RL and show
how to reduce the human labor required by training a supervised learner to
imitate the human's intervention decisions. We evaluate this scheme on Atari
games, with a Deep RL agent being overseen by a human for four hours. When the
class of catastrophes is simple, we are able to prevent all catastrophes
without affecting the agent's learning (whereas an RL baseline fails due to
catastrophic forgetting). However, this scheme is less successful when
catastrophes are more complex: it reduces but does not eliminate catastrophes
and the supervised learner fails on adversarial examples found by the agent.
Extrapolating to more challenging environments, we show that our implementation
would not scale (due to the infeasible amount of human labor required). We
outline extensions of the scheme that are necessary if we are to train
model-free agents without a single catastrophe
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich
environments, it is important to model subtle human behaviors and navigation
rules (e.g., passing on the right). However, while instinctive to humans,
socially compliant navigation is still difficult to quantify due to the
stochasticity in people's behaviors. Existing works are mostly focused on using
feature-matching techniques to describe and imitate human paths, but often do
not generalize well since the feature values can vary from person to person,
and even run to run. This work notes that while it is challenging to directly
specify the details of what to do (precise mechanisms of human navigation), it
is straightforward to specify what not to do (violations of social norms).
Specifically, using deep reinforcement learning, this work develops a
time-efficient navigation policy that respects common social norms. The
proposed method is shown to enable fully autonomous navigation of a robotic
vehicle moving at human walking speed in an environment with many pedestrians.Comment: 8 page
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