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Run an experiment to evaluate the performance of a simulated annealing gradient descent (SA-GD) approach compared to traditional gradient descent (GD). The purpose of this experiment is to understand …
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The project aims to develop a reinforcement learning (RL) agent to optimize waste collection in a simulated environment, minimizing overflow events and improving efficiency.
Environment and State R…
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### Description
The project aims to develop a reinforcement learning (RL) agent to optimize waste collection in a simulated environment, minimizing overflow events and improving efficiency.
Environm…
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The current strategy assigns exploitation and exploration weights to clusters in the following manner:
![image](https://user-images.githubusercontent.com/7997790/50731117-92257780-1122-11e9-940c-51…
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It seems only the optimizer parameters of the alive gaussians that are sampled are reset, while the optimizer parameters of the dead gaussians remain unchanged. May I ask the reason for this？
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Here are ways that I see mlrMBO currently offering control over exploration vs exploitation for single-objective tuning:
- The infill criterion offers a discrete set of choices, each of which impli…
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- Exploitation is the right thing to do to maximize the expected reward on the one step, but
exploration may produce the greater total reward in the long run.
- Reward is lower in the short run, dur…
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https://en.wikipedia.org/wiki/Multi-armed_bandit
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With the models grouping introduced in {{3.34}}, the {{exploitation_ratio}} doesn’t apply as strictly as it did before.
Pre {{3.34}}, the exploitation ratio was dedicated to tuning of learn-rate on t…