99 citations · 227 across the 34 of their papers we have counts for
38 papers
A Theoretical Justification for Asymmetric Actor-Critic Algorithms
Gaspard Lambrechts, Damien Ernst, Aditya Mahajan
In reinforcement learning for partially observable environments, many successful algorithms have been developed within the asymmetric learning paradigm. This paradigm leverages add…
Agent-state based policies in POMDPs: Beyond belief-state MDPs
Amit Sinha, Aditya Mahajan
The traditional approach to POMDPs is to convert them into fully observed MDPs by considering a belief state as an information state. However, a belief-state based approach require…
Periodic agent-state based Q-learning for POMDPs
Amit Sinha, Matthieu Geist, Aditya Mahajan
The standard approach for Partially Observable Markov Decision Processes (POMDPs) is to convert them to a fully observed belief-state MDP. However, the belief state depends on the…
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni, Benjamin Eysenbach, Erfan Seyedsalehi +4
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs…
Mean-field games among teams
Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan
In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with…
Approximate information state based convergence analysis of recurrent Q-learning
Erfan Seyedsalehi, Nima Akbarzadeh, Amit Sinha +1
In spite of the large literature on reinforcement learning (RL) algorithms for partially observable Markov decision processes (POMDPs), a complete theoretical understanding is stil…