Learning to Coordinate with Deep Reinforcement Learning in Doubles Pong Game

Diallo, Elhadji Amadou Oury; Sugiyama, Ayumi; Sugawara, Toshiharu

doi:10.1109/icmla.2017.0-184

Cited by 24 publications

(12 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through this independence degree, the agent learns to decide whether it needs to act independently or cooperate with other agents in different circumstances. Likewise, Diallo et al [18] extended DQN to a multi-agent concurrent DQN and demonstrated that this method can converge in a non-stationary environment. Foerster et al [25] alternatively introduced two methods for stabilising experience replay of DQN in MADRL.…”

Section: Non-stationaritymentioning

confidence: 99%

“…Value-based Actor-critic Policy-based Partial observability DRQN [36]; DDRQN [24]; RIAL and DIAL [23]; Action-specific DRQN [121]; MT-MARL [85]; PS-DQN [30]; RL as a Rehearsal (RLaR) [55] PS-DDPG and PS-A3C [30]; MADDPG-M [48] DPIQN and DRPIQN [42]; PS-TRPO [30]; Bayesian action decoder (BAD) [26] Nonstationarity DRUQN and DLCQN [12]; Multi-agent concurrent DQN [18]; Recurrent DQN-based multi-agent importance sampling and fingerprints [25]; Hysteretic-DQN [85]; Lenient-DQN [86]; WDDQN [120] MADDPG [68]; PS-A3C [30] PS-TRPO [30]…”

Section: Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Nguyen

Nahavandi

2020

IEEE Trans. Cybern.

747

309

View full text Add to dashboard Cite

Reinforcement learning (RL) algorithms have been around for decades and employed to solve various sequential decision-making problems. These algorithms however have faced great challenges when dealing with high-dimensional environments. The recent development of deep learning has enabled RL methods to drive optimal policies for sophisticated and capable agents, which can perform efficiently in these challenging environments. This paper addresses an important aspect of deep RL related to situations that require multiple agents to communicate and cooperate to solve complex tasks. A survey of different approaches to problems related to multi-agent deep RL (MADRL) is presented, including non-stationarity, partial observability, continuous state and action spaces, multi-agent training schemes, multi-agent transfer learning. The merits and demerits of the reviewed methods will be analyzed and discussed, with their corresponding applications explored. It is envisaged that this review provides insights about various MADRL methods and can lead to future development of more robust and highly useful multi-agent learning methods for solving real-world problems.

show abstract

Section: Non-stationaritymentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Nguyen

Nahavandi

2020

IEEE Trans. Cybern.

747

309

View full text Add to dashboard Cite

show abstract

“…In [85], Elhadji et al embedded MADRL in distributed agents, which are computer players in a pong game (A.6). The agents have global goals (i.e., winning as a team) and independent decision-making capabilities influenced by one another's decisions.…”

Section: Elhadji's Madrl With Concurrent Learningmentioning

confidence: 99%

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

et al. 2021

View full text Add to dashboard Cite

Recent advancements in deep reinforcement learning (DRL) have led to its application in multi-agent scenarios to solve complex real-world problems, such as network resource allocation and sharing, network routing, and traffic signal controls. Multi-agent DRL (MADRL) enables multiple agents to interact with each other and with their operating environment, and learn without the need for external critics (or teachers), thereby solving complex problems. Significant performance enhancements brought about by the use of MADRL have been reported in multi-agent domains; for instance, it has been shown to provide higher quality of service (QoS) in network resource allocation and sharing. This paper presents a survey of MADRL models that have been proposed for various kinds of multi-agent domains, in a taxonomic approach that highlights various aspects of MADRL models and applications, including objectives, characteristics, challenges, applications, and performance measures. Furthermore, we present open issues and future directions of MADRL.

show abstract

“…DRUQN tries to avoid policy bias by updating the value of the action inversely proportional to the probability of selecting that action. Diallo et al [130] proposed a multiagent concurrent DQN algorithm able to converge in a nonstationary environment. Lenient-DQN conceived by Palmer et al [131] utilizes leniency with decaying temperature values for adjusting the policy updates sampled from the experience replay memory to deal with the non-stationarity caused by concurrent learning.…”

Section: ) Multi-agent Reinforcement Learningmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹

2021

Preprint

View full text Add to dashboard Cite

<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

show abstract

Learning to Coordinate with Deep Reinforcement Learning in Doubles Pong Game

Cited by 24 publications

References 13 publications

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Contact Info

Product

Resources

About