<p>Specific hyperparameters for <em>Reacher-v2</em>, from Table 10 in <a href="https://arxiv.org/abs/2001.04025" rel="nofollow noreferrer">Universal Successor Features for Transfer Reinforcement Learning</a>:</p>
<div class="s-table-container">
<table class="s-table">
<thead>
<tr>
<th style="text-align: left;">Hyperparameter</th>
<th style="text-align: center;">DDPG</th>
<th style="text-align: center;">DDPG + USFs</th>
<th style="text-align: center;">HER</th>
<th style="text-align: center;">HER + USFs</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align: left;">Actor Learning Rate</td>
<td style="text-align: center;">1e-4</td>
<td style="text-align: center;">1e-4</td>
<td style="text-align: center;">1e-3</td>
<td style="text-align: center;">1e-4</td>
</tr>
<tr>
<td style="text-align: left;">Critic Learning Rate</td>
<td style="text-align: center;">1e-3</td>
<td style="text-align: center;">1e-3</td>
<td style="text-align: center;">1e-4</td>
<td style="text-align: center;">1e-3</td>
</tr>
<tr>
<td style="text-align: left;">Loss Weight λ</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">1e-4</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">0.01</td>
</tr>
<tr>
<td style="text-align: left;">Batch Size</td>
<td style="text-align: center;">64</td>
<td style="text-align: center;">64</td>
<td style="text-align: center;">64</td>
<td style="text-align: center;">64</td>
</tr>
<tr>
<td style="text-align: left;">Discount Factor γ</td>
<td style="text-align: center;">0.99</td>
<td style="text-align: center;">0.99</td>
<td style="text-align: center;">0.99</td>
<td style="text-align: center;">0.99</td>
</tr>
<tr>
<td style="text-align: left;">HER Future Steps</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">50</td>
<td style="text-align: center;">50</td>
</tr>
<tr>
<td style="text-align: left;">HER Buffer Sampling Probability</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">N/A</td>
<td style="text-align: center;">0.5</td>
<td style="text-align: center;">0.5</td>
</tr>
</tbody>
</table>
</div>
CC BY-SA 4.0
2022-07-28T21:01:23.033Z
2022-07-28T21:02:11.713Z
2022-07-28T21:02:11.713Z
kir0ul
<p>This is kind of spread throughout the internet. There are tons of <a href="https://gist.github.com/gkhayes/3d154e0505e31d6367be22ed3da2e955" rel="nofollow noreferrer">gists</a> and <a href="https://github.com/sourcecode369/deep-reinforcement-learning" rel="nofollow noreferrer">repos</a>, there are contests and topics on kaggle. I do not think there is one centralized repository of this sort of thing other than individuals who have curated some list(which is the repo I linked), although perhaps you could make one!</p>

CC BY-SA 4.0
2022-07-22T20:08:30.977Z
2022-07-22T20:08:30.977Z
hisairnessag3
<p>Is there any place where people share their agent's settings for solving OpenAI Gym Environments?</p>
<p>For example, I'd like to know what are good parameters for a DDPG agent to learn the task in Reacher-v2. I believe that a lot of people tried to solve it and maybe they shared their solution for achieving better performance.</p>

<reinforcement-learning><deep-rl><open-ai><ddpg><gym>
RL solutions for OpenAI Gym environments?
pippo
# RL solutions for OpenAI Gym environments?

(Asked by: username_0 on 2022-07-22)

Is there any place where people share their agent's settings for solving OpenAI Gym Environments?

For example, I'd like to know what are good parameters for a DDPG agent to learn the task in Reacher-v2. I believe that a lot of people tried to solve it and maybe they shared their solution for achieving better performance.

**Tags:** reinforcement-learning, deep-rl, open-ai, ddpg, gym


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## 2 answers

### Answer by username_1 on 2022-07-28. Score: 1

Specific hyperparameters for *Reacher-v2*, from Table 10 in [Universal Successor Features for Transfer Reinforcement Learning](https://arxiv.org/abs/2001.04025):

| Hyperparameter | DDPG | DDPG + USFs | HER | HER + USFs |
| --- | --- | --- | --- | --- |
| Actor Learning Rate | 1e-4 | 1e-4 | 1e-3 | 1e-4 |
| Critic Learning Rate | 1e-3 | 1e-3 | 1e-4 | 1e-3 |
| Loss Weight λ | N/A | 1e-4 | N/A | 0.01 |
| Batch Size | 64 | 64 | 64 | 64 |
| Discount Factor γ | 0.99 | 0.99 | 0.99 | 0.99 |
| HER Future Steps | N/A | N/A | 50 | 50 |
| HER Buffer Sampling Probability | N/A | N/A | 0.5 | 0.5 |


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### Answer by username_2 on 2022-07-22. Score: 0

This is kind of spread throughout the internet. There are tons of [gists](https://gist.github.com/gkhayes/3d154e0505e31d6367be22ed3da2e955) and [repos](https://github.com/sourcecode369/deep-reinforcement-learning), there are contests and topics on kaggle. I do not think there is one centralized repository of this sort of thing other than individuals who have curated some list(which is the repo I linked), although perhaps you could make one!


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