9 citations · 18 across the 5 of their papers we have counts for
5 papers
Latent Interactive A2C for Improved RL in Open Many-Agent Systems
Keyang He, Prashant Doshi, Bikramjit Banerjee
There is a prevalence of multiagent reinforcement learning (MARL) methods that engage in centralized training. But, these methods involve obtaining various types of information fro…
Many Agent Reinforcement Learning Under Partial Observability
Keyang He, Prashant Doshi, Bikramjit Banerjee
Recent renewed interest in multi-agent reinforcement learning (MARL) has generated an impressive array of techniques that leverage deep reinforcement learning, primarily actor-crit…
Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards
Keyang He, Bikramjit Banerjee, Prashant Doshi
Consider a typical organization whose worker agents seek to collectively cooperate for its general betterment. However, each individual agent simultaneously seeks to act to secure…
Maximum Entropy Multi-Task Inverse RL
Saurabh Arora, Bikramjit Banerjee, Prashant Doshi
Multi-task IRL allows for the possibility that the expert could be switching between multiple ways of solving the same problem, or interleaving demonstrations of multiple tasks. Th…
A Framework and Method for Online Inverse Reinforcement Learning
Saurabh Arora, Prashant Doshi, Bikramjit Banerjee
Inverse reinforcement learning (IRL) is the problem of learning the preferences of an agent from the observations of its behavior on a task. While this problem has been well invest…