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Detail
Journal Article
Avoiding collaborative paradox in multi‐agent reinforcement learning
- Authors
- Hyunseok Kim, Seonghyun Kim, Donghun Lee, Ingook Jang
- Issue Date
- 2021-12
- Citation
- ETRI Journal, v.43, no.6, pp.1004-1012
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.
- KSP Keywords
- Collaborative environment, Collaborative task, High dispersion, Learning approach, Nonstationary Environment, Real-world applications, Reinforcement Learning(RL), multi-agent reinforcement learning, partial observations, social loafing, task completion
This work is distributed under the term of Korea Open Government License (KOGL)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
(Type 4: : Type 1 + Commercial Use Prohibition+Change Prohibition)
