In this paper, we formulate agent's decision process under the framework of Markov decision processes, and in particular, the multi-agent extension to Markov decision process...
In multi-agent MDPs, it is generally necessary to consider the joint state space of all agents, making the size of the problem and the solution exponential in the number of agents...
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function....
We adopt the decision-theoretic principle of expected utility maximization as a paradigm for designing autonomous rational agents operating in multi-agent environments. We use the...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...