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- W4387194045 abstract "Multi-Agent Reinforcement Learning (MARL) in the recent past, has been used to model many real-life applications and perhaps even more recently. This Multi-Agent extension of Reinforcement Learning (RL) is heavily used to model those kinds of scenarios where the information regarding the environment is very limited. Off-policy RL and Off-policy MARL methods uses a memory to save the transitions (experiences) of the agent(s) and later samples them, in a particular strategy, for training the networks. Prioritized Experience Replay (PER) is a particular procedure which samples transitions on the account of the error(magnitude) a particular transition has during training. For a transition, the probability of it getting sampled for training gets higher with a higher error value. The method is effective in terms of giving better results early on and helps the model to learn policies quickly. In PER, it is important to strike a balance between priority and diversity of samples which it does by a prioritization factor. For our work in this paper, we propose and test a Controlled Partial PER(CPPER) where we sample transitions from the memory partly on the basis of PER and the remaining, uniformly with a control, as a result of which a further more diverse batch of transition is sampled. We performed experiments on Multi-Agent Particle Environment (MPE) for the proposed method and the results obtained were propitious." @default.
- W4387194045 created "2023-09-30" @default.
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- W4387194045 date "2023-04-21" @default.
- W4387194045 modified "2023-09-30" @default.
- W4387194045 title "CPPER: A Controlled Partial Prioritized Experience Replay for Reinforcement Learning in its Multi- Agent extension" @default.
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- W4387194045 doi "https://doi.org/10.1109/inc457730.2023.10263207" @default.
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