1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
DROGO: Default Representation Objective via Graph Optimization in Reinforcement Learning
Hon Tik Tse, Marlos C. Machado
In computational reinforcement learning, the default representation (DR) and its principal eigenvector have been shown to be effective for a wide variety of applications, including…
cs.LG2025
Reward-Aware Proto-Representations in Reinforcement Learning
Hon Tik Tse, Siddarth Chandrasekar, Marlos C. Machado
In recent years, the successor representation (SR) has attracted increasing attention in reinforcement learning (RL), and it has been used to address some of its key challenges, su…
cs.LG2022★ 1 cited
Exploiting Semantic Epsilon Greedy Exploration Strategy in Multi-Agent Reinforcement Learning
Hon Tik Tse, Ho-fung Leung
Multi-agent reinforcement learning (MARL) can model many real world applications. However, many MARL approaches rely on epsilon greedy for exploration, which may discourage visitin…