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Crossmodal Attentive Skill Learner
This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated
with the recently-introduced Asynchronous Advantage Option-Critic (A2OC)
architecture [Harb et al., 2017] to enable hierarchical reinforcement learning
across multiple sensory inputs. We provide concrete examples where the approach
not only improves performance in a single task, but accelerates transfer to new
tasks. We demonstrate the attention mechanism anticipates and identifies useful
latent features, while filtering irrelevant sensor modalities during execution.
We modify the Arcade Learning Environment [Bellemare et al., 2013] to support
audio queries, and conduct evaluations of crossmodal learning in the Atari 2600
game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017],
we open-source a fast hybrid CPU-GPU implementation of CASL.Comment: International Conference on Autonomous Agents and Multiagent Systems
(AAMAS) 2018, NIPS 2017 Deep Reinforcement Learning Symposiu
Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has been applied successfully to many
robotic applications. However, the large number of trials needed for training
is a key issue. Most of existing techniques developed to improve training
efficiency (e.g. imitation) target on general tasks rather than being tailored
for robot applications, which have their specific context to benefit from. We
propose a novel framework, Assisted Reinforcement Learning, where a classical
controller (e.g. a PID controller) is used as an alternative, switchable policy
to speed up training of DRL for local planning and navigation problems. The
core idea is that the simple control law allows the robot to rapidly learn
sensible primitives, like driving in a straight line, instead of random
exploration. As the actor network becomes more advanced, it can then take over
to perform more complex actions, like obstacle avoidance. Eventually, the
simple controller can be discarded entirely. We show that not only does this
technique train faster, it also is less sensitive to the structure of the DRL
network and consistently outperforms a standard Deep Deterministic Policy
Gradient network. We demonstrate the results in both simulation and real-world
experiments.Comment: Published in ICRA2018. The code is now available at
https://github.com/xie9187/AsDDP
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