18 citations · 40 across the 5 of their papers we have counts for
5 papers
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
Heechang Ryu, Hayong Shin, Jinkyoo Park
Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than training a single-agent reinforcement learning algorithm, because the result of a multi-agen…
REMAX: Relational Representation for Multi-Agent Exploration
Heechang Ryu, Hayong Shin, Jinkyoo Park
Training a multi-agent reinforcement learning (MARL) model with a sparse reward is generally difficult because numerous combinations of interactions among agents induce a certain o…
Does Adam optimizer keep close to the optimal point?
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. Recent studies have found the r…
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
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 transferabi…
Multi-Agent Actor-Critic with Generative Cooperative Policy Network
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. Mainly, we are focused…