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cs.LG2021
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…
cs.LG2020
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…