9 citations · 12 across the 5 of their papers we have counts for
5 papers
Information Content Exploration
Jacob Chmura, Hasham Burhani, Xiao Qi Shi
Sparse reward environments are known to be challenging for reinforcement learning agents. In such environments, efficient and scalable exploration is crucial. Exploration is a mean…
Scope Loss for Imbalanced Classification and RL Exploration
Hasham Burhani, Xiao Qi Shi, Jonathan Jaegerman +1
We demonstrate equivalence between the reinforcement learning problem and the supervised classification problem. We consequently equate the exploration exploitation trade-off in re…
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis +1
Advances in Reinforcement Learning (RL) span a wide variety of applications which motivate development in this area. While application tasks serve as suitable benchmarks for real w…
Energy-based Surprise Minimization for Multi-Agent Value Factorization
Karush Suri, Xiao Qi Shi, Konstantinos Plataniotis +1
Multi-Agent Reinforcement Learning (MARL) has demonstrated significant success in training decentralised policies in a centralised manner by making use of value factorization metho…
Maximum Mutation Reinforcement Learning for Scalable Control
Karush Suri, Xiao Qi Shi, Konstantinos N. Plataniotis +1
Advances in Reinforcement Learning (RL) have demonstrated data efficiency and optimal control over large state spaces at the cost of scalable performance. Genetic methods, on the o…