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Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion
We introduce Wasserstein Adversarial Proximal Policy Optimization (WAPPO...
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A Critical Investigation of Deep Reinforcement Learning for Navigation
The navigation problem is classically approached in two steps: an explor...
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AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning
Dialogue policy plays an important role in task-oriented spoken dialogue...
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Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation
This paper proposes a navigation algorithm ori- ented to multi-agent dyn...
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Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation
Deep Reinforcement Learning has managed to achieve state-of-the-art resu...
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Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
We present a target-driven navigation system to improve mapless visual n...
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Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations
Being able to navigate to a target with minimal supervision and prior kn...
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VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation
In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator. Specifically, we build on the concept of Universal Successor Features with an A3C agent. We introduce the novel architectural contribution of a Successor Feature Dependant Policy (SFDP) and adopt the concept of Variational Information Bottlenecks to achieve state of the art performance. VUSFA, our final architecture, is a straightforward approach that can be implemented using our open source repository. Our approach is generalizable, showed greater stability in training, and outperformed recent approaches in terms of transfer learning ability.
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