Latent Interactive A2C for Improved RL in Open Many-Agent Systems

no code implementations • 9 May 2023 • Keyang He, Prashant Doshi, Bikramjit Banerjee

There is a prevalence of multiagent reinforcement learning (MARL) methods that engage in centralized training.

Many Agent Reinforcement Learning Under Partial Observability

no code implementations • 17 Jun 2021 • 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-critic architectures, and can be applied to a limited range of settings in terms of observability and communication.

Multi-agent Reinforcement Learning reinforcement-learning +1

Cooperative-Competitive Reinforcement Learning with History-Dependent Rewards

no code implementations • 15 Oct 2020 • Keyang He, Bikramjit Banerjee, Prashant Doshi

As such, the individual agent in the organization must cooperate and compete.

Decision Making Multi-agent Reinforcement Learning +2

Maximum Entropy Multi-Task Inverse RL

1 code implementation • 27 Apr 2020 • Saurabh Arora, Bikramjit Banerjee, Prashant Doshi

The learner aims to learn the multiple reward functions that guide these ways of solving the problem.

Clustering

A Framework and Method for Online Inverse Reinforcement Learning

no code implementations • 21 May 2018 • 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.

reinforcement-learning Reinforcement Learning (RL)