4,741 research outputs found
Towards Better Interpretability in Deep Q-Networks
Deep reinforcement learning techniques have demonstrated superior performance
in a wide variety of environments. As improvements in training algorithms
continue at a brisk pace, theoretical or empirical studies on understanding
what these networks seem to learn, are far behind. In this paper we propose an
interpretable neural network architecture for Q-learning which provides a
global explanation of the model's behavior using key-value memories, attention
and reconstructible embeddings. With a directed exploration strategy, our model
can reach training rewards comparable to the state-of-the-art deep Q-learning
models. However, results suggest that the features extracted by the neural
network are extremely shallow and subsequent testing using out-of-sample
examples shows that the agent can easily overfit to trajectories seen during
training.Comment: Accepted at AAAI-19; (16 pages, 18 figures
Learn to Interpret Atari Agents
Deep Reinforcement Learning (DeepRL) agents surpass human-level performances
in a multitude of tasks. However, the direct mapping from states to actions
makes it hard to interpret the rationale behind the decision making of agents.
In contrast to previous a-posteriori methods of visualizing DeepRL policies, we
propose an end-to-end trainable framework based on Rainbow, a representative
Deep Q-Network (DQN) agent. Our method automatically learns important regions
in the input domain, which enables characterizations of the decision making and
interpretations for non-intuitive behaviors. Hence we name it Region Sensitive
Rainbow (RS-Rainbow). RS-Rainbow utilizes a simple yet effective mechanism to
incorporate visualization ability into the learning model, not only improving
model interpretability, but leading to improved performance. Extensive
experiments on the challenging platform of Atari 2600 demonstrate the
superiority of RS-Rainbow. In particular, our agent achieves state of the art
at just 25% of the training frames. Demonstrations and code are available at
https://github.com/yz93/Learn-to-Interpret-Atari-Agents
Beyond saliency: understanding convolutional neural networks from saliency prediction on layer-wise relevance propagation
Despite the tremendous achievements of deep convolutional neural networks
(CNNs) in many computer vision tasks, understanding how they actually work
remains a significant challenge. In this paper, we propose a novel two-step
understanding method, namely Salient Relevance (SR) map, which aims to shed
light on how deep CNNs recognize images and learn features from areas, referred
to as attention areas, therein. Our proposed method starts out with a
layer-wise relevance propagation (LRP) step which estimates a pixel-wise
relevance map over the input image. Following, we construct a context-aware
saliency map, SR map, from the LRP-generated map which predicts areas close to
the foci of attention instead of isolated pixels that LRP reveals. In human
visual system, information of regions is more important than of pixels in
recognition. Consequently, our proposed approach closely simulates human
recognition. Experimental results using the ILSVRC2012 validation dataset in
conjunction with two well-established deep CNN models, AlexNet and VGG-16,
clearly demonstrate that our proposed approach concisely identifies not only
key pixels but also attention areas that contribute to the underlying neural
network's comprehension of the given images. As such, our proposed SR map
constitutes a convenient visual interface which unveils the visual attention of
the network and reveals which type of objects the model has learned to
recognize after training. The source code is available at
https://github.com/Hey1Li/Salient-Relevance-Propagation.Comment: 35 pages, 15 figure
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