74 citations · 279 across the 24 of their papers we have counts for
26 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…
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy
Kenneth Bogert, Yikang Gui, Prashant Doshi
The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically…
SIPOMDPLite-Net: Lightweight, Self-Interested Learning and Planning in POSGs with Sparse Interactions
Gengyu Zhang, Prashant Doshi
This work introduces sIPOMDPLite-net, a deep neural network (DNN) architecture for decentralized, self-interested agent control in partially observable stochastic games (POSGs) wit…
Marginal MAP Estimation for Inverse RL under Occlusion with Observer Noise
Prasanth Sengadu Suresh, Prashant Doshi
We consider the problem of learning the behavioral preferences of an expert engaged in a task from noisy and partially-observable demonstrations. This is motivated by real-world ap…
A Hierarchical Bayesian model for Inverse RL in Partially-Controlled Environments
Kenneth Bogert, Prashant Doshi
Robots learning from observations in the real world using inverse reinforcement learning (IRL) may encounter objects or agents in the environment, other than the expert, that cause…
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…