A survey and critique of multiagent deep reinforcement learning
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
[PDF][PDF] Is multiagent deep reinforcement learning the answer or the question? A brief survey
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …
led to a dramatic increase in the number of applications and methods. Recent works have …
Progress and summary of reinforcement learning on energy management of MPS-EV
The escalating environmental concerns and energy crisis caused by internal combustion
engines (ICE) have become unacceptable under environmental regulations and the energy …
engines (ICE) have become unacceptable under environmental regulations and the energy …
Towards robust and domain agnostic reinforcement learning competitions: MineRL 2020
Reinforcement learning competitions have formed the basis for standard research
benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the …
benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the …
Bombalytics: Visualization of competition and collaboration strategies of players in a bomb laying game
Competition and collaboration form complex interaction patterns between the agents and
objects involved. Only by understanding these interaction patterns, we can reveal the …
objects involved. Only by understanding these interaction patterns, we can reveal the …
On hard exploration for reinforcement learning: A case study in pommerman
How to best explore in domains with sparse, delayed, and deceptive rewards is an important
open problem for reinforcement learning (RL). This paper considers one such domain, the …
open problem for reinforcement learning (RL). This paper considers one such domain, the …
Analysis of statistical forward planning methods in Pommerman
D Perez-Liebana, RD Gaina, O Drageset… - Proceedings of the …, 2019 - ojs.aaai.org
Pommerman is a complex multi-player and partially observable game where agents try to be
the last standing to win. This game poses very interesting challenges to AI, such as …
the last standing to win. This game poses very interesting challenges to AI, such as …
Accelerating training in pommerman with imitation and reinforcement learning
The Pommerman simulation was recently developed to mimic the classic Japanese game
Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the …
Bomberman, and focuses on competitive gameplay in a multi-agent setting. We focus on the …
Efficient searching with MCTS and imitation learning: a case study in Pommerman
H Yang, S Li, X Xu, X Liu, Z Meng, Y Zhang - IEEE Access, 2021 - ieeexplore.ieee.org
Pommerman is a popular reinforcement learning environment because it imposes several
challenges such as sparse and deceptive rewards and delayed action effects. In this paper …
challenges such as sparse and deceptive rewards and delayed action effects. In this paper …
Developing a Successful Bomberman Agent
D Kowalczyk, J Kowalski, H Obrzut, M Maras… - arXiv preprint arXiv …, 2022 - arxiv.org
In this paper, we study AI approaches to successfully play a 2-4 players, full information,
Bomberman variant published on the CodinGame platform. We compare the behavior of …
Bomberman variant published on the CodinGame platform. We compare the behavior of …