26,388 research outputs found
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present the first collaborative multi-agent deep reinforcement learning
algorithm to learn the optimal policy for joint active object localization,
which effectively exploits such beneficial contextual information. We learn
inter-agent communication through cross connections with gates between the
Q-networks, which is facilitated by a novel multi-agent deep Q-learning
algorithm with joint exploitation sampling. We verify our proposed method on
multiple object detection benchmarks. Not only does our model help to improve
the performance of state-of-the-art active localization models, it also reveals
interesting co-detection patterns that are intuitively interpretable
Model Learning for Look-ahead Exploration in Continuous Control
We propose an exploration method that incorporates look-ahead search over
basic learnt skills and their dynamics, and use it for reinforcement learning
(RL) of manipulation policies . Our skills are multi-goal policies learned in
isolation in simpler environments using existing multigoal RL formulations,
analogous to options or macroactions. Coarse skill dynamics, i.e., the state
transition caused by a (complete) skill execution, are learnt and are unrolled
forward during lookahead search. Policy search benefits from temporal
abstraction during exploration, though itself operates over low-level primitive
actions, and thus the resulting policies does not suffer from suboptimality and
inflexibility caused by coarse skill chaining. We show that the proposed
exploration strategy results in effective learning of complex manipulation
policies faster than current state-of-the-art RL methods, and converges to
better policies than methods that use options or parametrized skills as
building blocks of the policy itself, as opposed to guiding exploration. We
show that the proposed exploration strategy results in effective learning of
complex manipulation policies faster than current state-of-the-art RL methods,
and converges to better policies than methods that use options or parameterized
skills as building blocks of the policy itself, as opposed to guiding
exploration.Comment: This is a pre-print of our paper which is accepted in AAAI 201
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
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