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This issue is for us to document how we will use reinforcement learning to train our rl-agent to avoid obstacles. The goal of this issue is:
- Learn more about the reward function
- Help decide wh…
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Hello.
I have a few questions. I would be grateful if you answer them.
Could you please tell me what the argument is and what it affects?
parser.add_argument('--smart_bob', action='store_tr…
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Thanks for your outstanding work. I would like to ask: how should we generate offline datasets, such as medium or medium-expert version, like D4RL. Also is it possible to render states into images to …
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## Overview
A first simple critique is that the code is left without comments in the parts regarding min-max and RL, as such the reader is offered no help understanding the trickier lines in the prog…
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## Weekly Notebook Entry — Week 4
### Overview
- **Week Span:** `9/9` to `9/15`
### Tasks for This Week
- [ ] Task 1: Give an overview of the models that were previously used (Multivariate Reg…
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It would be interesting to port a few basic communication environments/training procedures to Flow. In particular, a popular communications baselines is "Learning Multi-agent Communication with Backpr…
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Mobile Reconfigurable Intelligent Surfaces for NOMA Networks: Federated Learning Approaches. (arXiv:2105.09462v1 [cs.NI])
https://ift.tt/3oxru0U
A novel framework of reconfigurable intelligent surface…
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**Learning**
- We learn by interacting with our environment.
- In any learning scenario for e.g. driving a car, we are acutely aware of how our environment responds to what we do, and we seek to inf…
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**Problem:**
MIT has its course on deep learning MIT 6.S191 that is updated every year and is a high quality resource attented by many students around the globe.
**Duration:**
26 April 2022
*…
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