Search Results - "Oh, Junhyuk"

Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network by Seunghoon Hong, Junhyuk Oh, Honglak Lee, Bohyung Han

“…We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised…”
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A design testbed for eco-friendly corrugated setter tray packaging by Oh, Junhyuk, Choi, Woojin, Jee, Haeseong

“…Plain and simple, a corrugated cardboard material provides the right level of protection for goods in transit, which helps keep shipping costs down. As made…”
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Grandmaster level in StarCraft II using multi-agent reinforcement learning by Vinyals, Oriol, Babuschkin, Igor, Czarnecki, Wojciech M., Mathieu, Michaël, Dudzik, Andrew, Chung, Junyoung, Choi, David H., Powell, Richard, Ewalds, Timo, Georgiev, Petko, Oh, Junhyuk, Horgan, Dan, Kroiss, Manuel, Danihelka, Ivo, Huang, Aja, Sifre, Laurent, Cai, Trevor, Agapiou, John P., Jaderberg, Max, Vezhnevets, Alexander S., Leblond, Rémi, Pohlen, Tobias, Dalibard, Valentin, Budden, David, Sulsky, Yury, Molloy, James, Paine, Tom L., Gulcehre, Caglar, Wang, Ziyu, Pfaff, Tobias, Wu, Yuhuai, Ring, Roman, Yogatama, Dani, Wünsch, Dario, McKinney, Katrina, Smith, Oliver, Schaul, Tom, Lillicrap, Timothy, Kavukcuoglu, Koray, Hassabis, Demis, Apps, Chris, Silver, David

“…Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal,…”
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Deep Learning‐Assisted Design of Bilayer Nanowire Gratings for High‐Performance MWIR Polarizers by Lee, Junghyun, Oh, Junhyuk, Chi, Hyung‐gun, Lee, Minseok, Hwang, Jehwan, Jeong, Seungjin, Kang, Sang‐Woo, Jee, Haeseong, Bae, Hagyoul, Hyun, Jae‐Sang, Kim, Jun Oh, Kim, Bongjoong

“…Optical metamaterials have revolutionized imaging capabilities by manipulating light‐matter interactions at the nanoscale beyond the diffraction limit. Bilayer…”
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Deep Reinforcement Learning with Plasticity Injection by Nikishin, Evgenii, Oh, Junhyuk, Ostrovski, Georg, Lyle, Clare, Pascanu, Razvan, Dabney, Will, Barreto, André

“…A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn…”
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Preference Optimization as Probabilistic Inference by Abdolmaleki, Abbas, Piot, Bilal, Shahriari, Bobak, Springenberg, Jost Tobias, Hertweck, Tim, Joshi, Rishabh, Oh, Junhyuk, Bloesch, Michael, Lampe, Thomas, Heess, Nicolas, Buchli, Jonas, Riedmiller, Martin

“…Existing preference optimization methods are mainly designed for directly learning from human feedback with the assumption that paired examples (preferred vs…”
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Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies by Sohn, Sungryull, Oh, Junhyuk, Lee, Honglak

“…We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a…”
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Many-Goals Reinforcement Learning by Veeriah, Vivek, Oh, Junhyuk, Singh, Satinder

“…All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was…”
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Introducing Symmetries to Black Box Meta Reinforcement Learning by Kirsch, Louis, Flennerhag, Sebastian, van Hasselt, Hado, Friesen, Abram, Oh, Junhyuk, Chen, Yutian

“…Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the…”
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On Learning Intrinsic Rewards for Policy Gradient Methods by Zheng, Zeyu, Oh, Junhyuk, Singh, Satinder

“…In many sequential decision making tasks, it is challenging to design reward functions that help an RL agent efficiently learn behavior that is considered good…”
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Value Prediction Network by Oh, Junhyuk, Singh, Satinder, Lee, Honglak

“…This paper proposes a novel deep reinforcement learning (RL) architecture, called Value Prediction Network (VPN), which integrates model-free and model-based…”
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Pick Your Battles: Interaction Graphs as Population-Level Objectives for Strategic Diversity by Garnelo, Marta, Czarnecki, Wojciech Marian, Liu, Siqi, Tirumala, Dhruva, Oh, Junhyuk, Gidel, Gauthier, van Hasselt, Hado, Balduzzi, David

“…Strategic diversity is often essential in games: in multi-player games, for example, evaluating a player against a diverse set of strategies will yield a more…”
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Balancing Constraints and Rewards with Meta-Gradient D4PG by Calian, Dan A, Mankowitz, Daniel J, Zahavy, Tom, Xu, Zhongwen, Oh, Junhyuk, Levine, Nir, Mann, Timothy

“…Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint…”
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Generative Adversarial Self-Imitation Learning by Guo, Yijie, Oh, Junhyuk, Singh, Satinder, Lee, Honglak

“…This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the…”
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Discovering Reinforcement Learning Algorithms by Oh, Junhyuk, Hessel, Matteo, Czarnecki, Wojciech M, Xu, Zhongwen, van Hasselt, Hado, Singh, Satinder, Silver, David

“…Reinforcement learning (RL) algorithms update an agent's parameters according to one of several possible rules, discovered manually through years of research…”
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Meta-Gradient Reinforcement Learning with an Objective Discovered Online by Xu, Zhongwen, van Hasselt, Hado, Hessel, Matteo, Oh, Junhyuk, Singh, Satinder, Silver, David

“…Deep reinforcement learning includes a broad family of algorithms that parameterise an internal representation, such as a value function or policy, by a deep…”
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Discovery of Options via Meta-Learned Subgoals by Veeriah, Vivek, Zahavy, Tom, Hessel, Matteo, Xu, Zhongwen, Oh, Junhyuk, Kemaev, Iurii, van Hasselt, Hado, Silver, David, Singh, Satinder

“…Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster. However, despite prior work on this…”
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Self-Imitation Learning by Oh, Junhyuk, Guo, Yijie, Singh, Satinder, Lee, Honglak

“…This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This…”
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In-context Reinforcement Learning with Algorithm Distillation by Laskin, Michael, Wang, Luyu, Oh, Junhyuk, Parisotto, Emilio, Spencer, Stephen, Steigerwald, Richie, Strouse, DJ, Hansen, Steven, Filos, Angelos, Brooks, Ethan, Gazeau, Maxime, Sahni, Himanshu, Singh, Satinder, Mnih, Volodymyr

“…We propose Algorithm Distillation (AD), a method for distilling reinforcement learning (RL) algorithms into neural networks by modeling their training…”
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A Self-Tuning Actor-Critic Algorithm by Zahavy, Tom, Xu, Zhongwen, Veeriah, Vivek, Hessel, Matteo, Oh, Junhyuk, van Hasselt, Hado, Silver, David, Singh, Satinder

“…Reinforcement learning algorithms are highly sensitive to the choice of hyperparameters, typically requiring significant manual effort to identify…”
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