Autonomous agentsusing novelty based goal exploration are often efficient in environments that require exploration. However, they get attracted to various forms of distracting unlearnable regions. To solve this problem, absolute learning progress (ALP)has been used in reinforcement learning agents with predefined goal features and access to expert knowledge. This work extends those concepts to unsupervised image-based goal exploration. We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions . It clusters the goal space and estimates ALP for each cluster. These ALP estimates can then be used to detect the distracting regions, and build a prior that enables further goal sampling mechanisms to ignore them . We construct an image based environment with distractors , on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on interesting regions of the environment and drastically improve performances. The source code is available at URL
Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to predefined goal features or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend those concepts to unsupervised image-based goal exploration. We propose a framework that allows agents to autonomously identify and ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic URLetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches . We construct a rich 3D image based environment with distractors . Experiments on this environment show that agents using our framework successfully identify interesting regions of the environment , resulting in drastically improved performances. The source code is available at URL
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Autonomous agentsusing novelty based goal exploration are often efficient in environments that require exploration. However, they
Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to
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various forms of distracting unlearnable regions. To solve this problem, absolute learning progress (ALP)has been used in reinforcement learning agents with
noise or more generally to parts of the environments that cannot be controlled (distractors). When agents have access to
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and access to expert knowledge. This work extends
or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend
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present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to
propose a framework that allows agents to autonomously identify and
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. It clusters the goal space and estimates ALP for each cluster. These ALP estimates can then be used to detect the distracting regions, and build a prior that enables further goal sampling mechanisms to ignore them
to both improve overall performance and avoid catastrophic URLetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches
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an
a rich 3D
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, on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on
. Experiments on this environment show that agents using our framework successfully identify
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and drastically improve
, resulting in drastically improved