74 citations · 279 across the 24 of their papers we have counts for
10 papers · 1 filter
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…
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…
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 Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Saurabh Arora, Prashant Doshi
Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a…
Reinforcement Learning for Heterogeneous Teams with PALO Bounds
Roi Ceren, Prashant Doshi, Keyang He
We introduce reinforcement learning for heterogeneous teams in which rewards for an agent are additively factored into local costs, stimuli unique to each agent, and global rewards…
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…
Dynamic Sum Product Networks for Tractable Inference on Sequence Data (Extended Version)
Mazen Melibari, Pascal Poupart, Prashant Doshi +1
Sum-Product Networks (SPN) have recently emerged as a new class of tractable probabilistic graphical models. Unlike Bayesian networks and Markov networks where inference may be exp…