The Mountain Car Environment. Moreover, a curiosity-based experience replay method is incorporated to solve the sparse reward problem and improve the sample efficiency in reinforcement learning. In this paper, we address three critical challenges for this task in a reinforcement learning setting: the mode collapse, the delayed feedback, and the time-consuming warm-up for policy networks. 01/23/2018 ∙ by Ildefons Magrans de Abril, et al. 10 Strategies To Promote Curiosity In Learning. IEEE International Conference on Real-time Computing and Robotics(RCAR), 2016 Mobile robots exploration through cnn-based reinforcement learning. Example: Think-aloud while reading an illustrated picture book, watching a video, or even having a conversation.
Lei Tai, Haoyang Ye, Qiong Ye, Ming Liu pdf / bibtex: A Robot Exploration Strategy Based on Q-learning Network. The Intrinsic Curiosity Module is the system that helps us to generate curiosity. Frontiers in Neurorobotics, 7:25, 2014. The agent who manipulates a robotic hand, will be encouraged to explore opti-mal trajectories according to the failure experience.
Curiosity-driven RL for developmental learning (Schmidhuber, 2006) encourages the learning of appropriate skills. Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation. First formulated into an actual learning framework by scholars at the University of California, Berkeley, curiosity-driven learning was introduced as a concept of error with curiosity being the error itself so that the learning model slowly realises its actions/tasks. We propose a curiosity reward based on information theory principles and consistent with the animal instinct to maintain certain critical parameters within … Reinforcement learning (RL) is one of the most actively pursued research techniques of machine learning, in which an artificial agent receives a positive reward when it does something right, … ∙ ARAYA ∙ 0 ∙ share . Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. Authors: Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Jingjing Li, Yang Yang. (Submitted on 1 Aug 2019 ( v1 ), last revised 29 Aug 2019 (this version, v2)) Abstract: Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a … Aside from preparing the brain for learning, curiosity can also make learning a more rewarding experience for students. Curiosity-driven reinforcement learning with homeostatic regulation. Skill learning can be made more explicit by identifying learned skills (Barto et al., 2004) within the option frame-work (Sutton et al., 1999). Exploitation versus exploration is a critical topic in Reinforcement Learning. AI researchers are exploring different curiosity models to tackle the issue of rewards in reinforcement learning, where systems can explore curiosity as a motivator to strive in challenging tasks. Learning Gentle Object Manipulation with Curiosity-Driven Deep Reinforcement Learning Abstract Robots must know how to be gentle when they need to interact with fragile objects, or when the robot itself is prone to wear and tear.
We propose that curiosity serves the purpose of motivating latent learning. 40. Exploitation versus exploration is a critical topic in reinforcement learning. [Updated on 2020-06-17: Add “exploration via disagreement” in the “Forward Dynamics” section. Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots.
Our novel reinforcement learning (RL) framework consists of a state-of-the-art, low-level, reactive control layer, which controls the iCub while respecting constraints, and a high-level curious agent, which explores the iCub's state-action space through information gain maximization, learning a world model from experience, controlling the actual iCub hardware in real-time. The core objective of this approach is to maximize positive rewards through continuous striving; for example, scoring high to win in a game. Curiosity-driven Exploration by Self-supervised Prediction Deepak Pathak 1Pulkit Agrawal Alexei A. Efros Trevor Darrell1 Abstract In many real-world scenarios, rewards extrinsic to the agent are extremely sparse, or absent al-together. Keywords: meta-learning, exploration, curiosity TL;DR: Meta-learning curiosity algorithms by searching through a rich space of programs yields novel designs that generalize across very different reinforcement-learning domains. Researchers in the field of Reinforcement Learning have put a lot of thought into developing good systems for providing intrinsic rewards to agents which endow them with similar motivation as we find in nature’s agents. Artificial intelligence can learn for itself if we teach it to be curious.
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