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Building Generalizable Agents with a Realistic and Rich 3D Environment
[article]
2018
arXiv
pre-print
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Using a subset of houses in House3D, we show that reinforcement learning agents trained with an enhancement of different levels of augmentations perform much better in unseen environments than our baselines ...
The diversity in House3D opens the door towards scene-level augmentation, while the label-rich nature of House3D enables us to inject pixel- & task-level augmentations such as domain randomization (Toubin ...
A number of recent works also use deep reinforcement learning for navigation in simulated 3D scenes. ...
arXiv:1801.02209v2
fatcat:27gzhfbhbbeknbwmeie5sg5nnm
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning
[article]
2021
arXiv
pre-print
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Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. ...
TSCI model is applicable to recurrent agents and can be used to discover causal features with high efficiency once trained. ...
Jimenez Rezende, “Towards interpretable reinforcement learning via attention in atari agents,” in 2019 IEEE/CVF International Confer-
using attention augmented agents,” in Advances ...
arXiv:2112.03020v1
fatcat:qdypudi6djcanelgijzh3ifkqq
Show, attend and interact: Perceivable human-robot social interaction through neural attention Q-network
2017
2017 IEEE International Conference on Robotics and Automation (ICRA)
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2017 pre-print arXiv:1702.08626v1 fatcat:yxlzndcplzgnrefomxe47g7pfq
We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world ...
Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement ...
Recent advancements in machine learning has combined deep learning with reinforcement learning and has led to the development of Deep Q-Network (DQN) [4] . ...
doi:10.1109/icra.2017.7989193
dblp:conf/icra/QureshiNYI17
fatcat:op4o2nah35hm5luy2plaavx2f4
Interactive Reinforcement Learning for Object Grounding via Self-Talking
[article]
2017
arXiv
pre-print
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During interactive training, both agents are reinforced by the guidance from a common reward function. ...
However, we observe language drifting problem during training and propose to use reward engineering to improve the interpretability for the generated conversations. ...
The question generator and the answer models are collectively tuned by a common reward function using reinforcement learning. ...
arXiv:1712.00576v1
fatcat:gtq6b2dbc5b7pdrydo6s5j7bku
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making – A Review
[article]
2019
arXiv
pre-print
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We discuss using different types of extended realities, such as digital twins, to train reinforcement learning agents to learn specific tasks through generalization. ...
Once generalization is achieved, we discuss how these can be used to develop self-learning agents. ...
Assuming that the cortical conductor theory interpretation is correct [18] , we can reduce an autonomous system into four main concepts that feeds an attention layer, learning, reasoning, control, and ...
arXiv:1910.08942v1
fatcat:wbpy3iijhbfwxeatiy4ztt5f2m
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Building intelligent agents that can communicate with and learn from humans in natural language is of great value. ...
The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the justlearned knowledge in subsequent conversations in a one-shot fashion. ...
The learner can ask about the new class and use the interpreter to extract useful information from the teacher's sentence via word-level attention η and content importance g mem jointly. ...
doi:10.18653/v1/p18-1243
dblp:conf/acl/XuYZ18
fatcat:tq2ddmukovgqnfevi4payy36qe
Memory-Augmented Reinforcement Learning for Image-Goal Navigation
[article]
2022
arXiv
pre-print
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2021 pre-print arXiv:2101.05181v3 fatcat:ho6npgow6ncmrdkfms2gj5fcwaOther Versions
Our method is based on an attention-based end-to-end model that leverages an episodic memory to learn to navigate. ...
In this work, we present a memory-augmented approach for image-goal navigation. ...
Given the difficulty to interpret raw RGB observations, the agent needs to learn a more informative representation than pixels. ...
arXiv:2101.05181v4
fatcat:v5mva4caxndu7k77iro5slgnhu
Augmented Replay Memory in Reinforcement Learning With Continuous Control
[article]
2019
arXiv
pre-print
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Online reinforcement learning agents are currently able to process an increasing amount of data by converting it into a higher order value functions. ...
The similar dynamics are implemented by a proposed augmented memory replay AMR capable of optimizing the replay of the experiences from the agent's memory structure by altering or augmenting their relevance ...
In the experiments reported in this paper, the augmentation dynamics are evolved over generations of learning agents performing reinforcement learning tasks in various environments. ...
arXiv:1912.12719v1
fatcat:ut5iv2ghsrdmtnwdzzcr6bbtnq
Speaker-Follower Models for Vision-and-Language Navigation
[article]
2018
arXiv
pre-print
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We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction. ...
reasoning process using generic sequence models. ...
This work was partially supported by US DoD and DARPA XAI and D3M, NSF awards IIS-1833355, Oculus VR, and the Berkeley Artificial Intelligence Research (BAIR) Lab. ...
arXiv:1806.02724v2
fatcat:t6g46qwawbgd3b4n3snyvzhywe
TarMAC: Targeted Multi-Agent Communication
[article]
2020
arXiv
pre-print
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We propose a targeted communication architecture for multi-agent reinforcement learning, where agents learn both what messages to send and whom to address them to while performing cooperative tasks in ...
Moreover, we show that the targeted communication strategies learned by agents are interpretable and intuitive. ...
Multi-Agent Reinforcement Learning (MARL). ...
arXiv:1810.11187v2
fatcat:gi4oogacaraizpdbce5qmittl4
Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles
[article]
2020
arXiv
pre-print
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Multi-agent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent. ...
Key words: Artificial General Intelligence (AGI), multiple intelligences, learning styles, physical intelligence, emotional intelligence, social intelligence, attentional intelligence, moral-ethical intelligence ...
In current AI systems, we extensively use six basic ways of learning: Supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, ensemble learning and deep learning ( ...
arXiv:2008.04793v4
fatcat:4l4wxa3bwnhlfbkfp2i63uwaou
Free-Lunch Saliency via Attention in Atari Agents
[article]
2019
arXiv
pre-print
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learning agents. ...
We propose a new approach to visualize saliency maps for deep neural network models and apply it to deep reinforcement learning agents trained on Atari environments. ...
Over the last decade, reinforcement learning has shifted towards deep reinforcement learning, where policies and/or models of the environment are modeled with deep neural networks, with impressive results ...
arXiv:1908.02511v2
fatcat:7iwnlsix7fgn7g46pl66a7pb3e
Interactive Language Acquisition with One-shot Visual Concept Learning through a Conversational Game
[article]
2018
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
The agent trained with this approach is able to actively acquire information by asking questions about novel objects and use the just-learned knowledge in subsequent conversations in a one-shot fashion ...
Building intelligent agents that can communicate with and learn from humans in natural language is of great value. ...
The learner can ask about the new class and use the interpreter to extract useful information from the teacher's sentence via word-level attention η and content importance g mem jointly. ...
arXiv:1805.00462v1
fatcat:exp4tpepobb2tf2uvydgqtx4je
Reinforcement Learning with Attention that Works: A Self-Supervised Approach
[article]
2019
arXiv
pre-print
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However previous attempts at integrating attention with reinforcement learning have failed to produce significant improvements. ...
We propose the first combination of self attention and reinforcement learning that is capable of producing significant improvements, including new state of the art results in the Arcade Learning Environment ...
attention with reinforcement learning. ...
arXiv:1904.03367v1
fatcat:5qlgijugpjfd3p6wzvb4m27u6y
Reinforcement Learning with Human Teachers: Understanding How People Want to Teach Robots
2006
ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication
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While Reinforcement Learning (RL) is not traditionally designed for interactive supervisory input from a human teacher, several works in both robot and software agents have adapted it for human input by ...
We report three main observations on how people administer feedback when teaching a robot a task through Reinforcement Learning: (a) they use the reward channel not only for feedback, but also for future-directed ...
Several examples of agents that learn interactively with a human teacher are based on Reinforcement Learning (RL). ...
doi:10.1109/roman.2006.314459
dblp:conf/ro-man/ThomazHB06
fatcat:hmssozyf7rdk7ghuzzi6vaiu34
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