Answers.Body: <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>
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-28T21:01:23.033Z
Answers.LastActivityDate: 2022-07-28T21:02:11.713Z
Answers.LastEditDate: 2022-07-28T21:02:11.713Z
Answers.UserDisplayName: kir0ul
Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-22T20:08:30.977Z
Answers.LastActivityDate: 2022-07-22T20:08:30.977Z
Answers.UserDisplayName: hisairnessag3
Body: <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>
  
Tags: <reinforcement-learning><deep-rl><open-ai><ddpg><gym>
Title: RL solutions for OpenAI Gym environments?
UserDisplayName: pippo
ThreadText: # 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
  
  
  ---
  
  ## 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 |
  
  
  ---
  
  ### 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!
  
  
  ---
  
  