Based on them, the authors proposed an agent learning appropriate actions in PD-like and non-PD-like games through self-evaluations in a previous paper [11].
The agent has two reward handling methods and two conditions for judging the game. We call the handled rewards self- evaluations and the reward handling methods ...
The agent has two reward handling methods and two conditions for judging the game. We call the handled rewards self- evaluations and the reward handling methods ...
New experiments are conducted in each of which the agents played a game having multiple states, and two kinds of game are included; the one notifies the ...
Bibliographic details on Self-evaluated Learning Agent in Multiple State Games.
People also ask
What is the multi agent learning theory?
How does multi-agent reinforcement learning work?
Apr 30, 2024 · We use this approach to evaluate state-of-the-art deep MARL algorithms on a class of negotiation games. From statistics on individual payoffs, ...
Multi-agent reinforcement learning (MARL) is a sub-field of reinforcement learning. It focuses on studying the behavior of multiple learning agents that ...
Sep 17, 2019 · Through training in our new simulated hide-and-seek environment, agents build a series of six distinct strategies and counterstrategies, some of ...
Sep 22, 2022 · Much of our work into training agents to play games is inspired by iterative self-play approaches like Fictitious. Play [32] and Double ...
Self-modifying policies (SMPs) trained by the success-story algorithm (SSA) have been successfully applied to various difficult reinforcement learning tasks ( ...