12 research outputs found
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
International audienceIn this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a selfsupervised fashion, and then use it to embed previously-visited states into the agent’s memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work
Mistral 7B
We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered
for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B
across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and
code generation. Our model leverages grouped-query attention (GQA) for faster
inference, coupled with sliding window attention (SWA) to effectively handle
sequences of arbitrary length with a reduced inference cost. We also provide a
model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses
the Llama 2 13B -- Chat model both on human and automated benchmarks. Our
models are released under the Apache 2.0 license.Comment: Models and code are available at
https://mistral.ai/news/announcing-mistral-7b
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
International audienceIn this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a selfsupervised fashion, and then use it to embed previously-visited states into the agent’s memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
International audienceIn this work, we present a memory-augmented approach for image-goal navigation. Earlier attempts, including RL-based and SLAM-based approaches have either shown poor generalization performance, or are heavily-reliant on pose/depth sensors. Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. First, we train a state-embedding network in a selfsupervised fashion, and then use it to embed previously-visited states into the agent’s memory. Our navigation policy takes advantage of this information through an attention mechanism. We validate our approach with extensive evaluations, and show that our model establishes a new state of the art on the challenging Gibson dataset. Furthermore, we achieve this impressive performance from RGB input alone, without access to additional information such as position or depth, in stark contrast to related work