Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning

no code implementations • 18 Jan 2021 • Heechang Ryu, Hayong Shin, Jinkyoo Park

We propose an algorithm that boosts MARL training using the biased action information of other agents based on a friend-or-foe concept.

Multi-agent Reinforcement Learning reinforcement-learning +1

REMAX: Relational Representation for Multi-Agent Exploration

no code implementations • 12 Aug 2020 • Heechang Ryu, Hayong Shin, Jinkyoo Park

To train the MARL model effectively without designing the intrinsic reward, we propose a learning-based exploration strategy to generate the initial states of a game.

Multi-agent Reinforcement Learning

Does Adam optimizer keep close to the optimal point?

no code implementations • 1 Nov 2019 • Kiwook Bae, Heechang Ryu, Hayong Shin

The adaptive optimizer for training neural networks has continually evolved to overcome the limitations of the previously proposed adaptive methods.

Multi-Agent Actor-Critic with Hierarchical Graph Attention Network

no code implementations • 27 Sep 2019 • Heechang Ryu, Hayong Shin, Jinkyoo Park

Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks.

Graph Attention Multi-agent Reinforcement Learning +1

Multi-Agent Actor-Critic with Generative Cooperative Policy Network

no code implementations • 22 Oct 2018 • Heechang Ryu, Hayong Shin, Jinkyoo Park

We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

Learning to Select: Problem, Solution, and Applications

no code implementations • ICLR 2018 • Heechang Ryu, Donghyun Kim, Hayong Shin

For example, job dispatching in the manufacturing factory is a typical "Learning to Select" problem.

Learning-To-Rank