5 citations · 21 across the 12 of their papers we have counts for
3 papers · 1 filter
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication
Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard
A challenge in reinforcement learning (RL) is minimizing the cost of sampling associated with exploration. Distributed exploration reduces sampling complexity in multi-agent RL (MA…
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard +1
Value function based reinforcement learning (RL) algorithms, for example, -learning, learn optimal policies from datasets of actions, rewards, and state transitions. However, wh…
Distributed Learning: Sequential Decision Making in Resource-Constrained Environments
Udari Madhushani, Naomi Ehrich Leonard
We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments. For distributed l…