From 710338889ea893728a69b0512e8b79da3d17e502 Mon Sep 17 00:00:00 2001 From: pseudo-rnd-thoughts Date: Tue, 19 Nov 2024 09:51:59 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Farama-F?= =?UTF-8?q?oundation/Gymnasium@79a2306dbab6f998b57953bfbc37fc2099c46683=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main/.buildinfo | 2 +- .../tutorials_python.zip | Bin 106133 -> 106133 bytes .../tutorials_jupyter.zip | Bin 128776 -> 128776 bytes main/introduction/train_agent/index.html | 2 +- main/searchindex.js | 2 +- 5 files changed, 3 insertions(+), 3 deletions(-) diff --git a/main/.buildinfo b/main/.buildinfo index 71fe12dcc..e5eec4b27 100644 --- a/main/.buildinfo +++ b/main/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 64598af6300d7778774d812f9bc8b693 +config: 5d23f1fbcf1219c393121f7e5e1731be tags: d77d1c0d9ca2f4c8421862c7c5a0d620 diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip index 3f98e7e3336b4788a83e52f920d4a4d7cdaaa8ea..447f3fc14fef26f44bcfa84b0d40904571fbbe27 100644 GIT binary patch delta 142 zcmbQbmu>1^Hr@blW)=|!5a9JI-pG4Kf*D9}z9W&44rbUl1Vn-vlWyh-fEmoMep!JS z+f*4<+(3-&>e-C!h9K5-!#Rx6U`GEO#$YhxDU4w~7b-GwE@J>#>d9P2UogXD9%CW^ D*)l7R delta 142 zcmbQbmu>1^Hr@blW)=|!5D<e-C!h9K5-!#Rx6U`GEO#$YhxDU4w~7b-GwE@J>#>d9P2UogXD9%CW^ DDD^DD diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip index fe437d981353ce5c0ab0507ebdb577061e9490b3..0e004e8003632690e8cddff5a0b02252daeee025 100644 GIT binary patch delta 144 zcmeBp$KLUdoj1UnnMH&F1k(MAH}c+;V+PWjU&Building an agent

Training the agent

-

To train the agent, we will let the agent play one episode (one complete game is called an episode) at a time and then update it’s Q-values after each episode. The agent will have to experience a lot of episodes to explore the environment sufficiently.

+

To train the agent, we will let the agent play one episode (one complete game is called an episode) at a time and update it’s Q-values after each action taken during the episode. The agent will have to experience a lot of episodes to explore the environment sufficiently.

# hyperparameters
 learning_rate = 0.01
 n_episodes = 100_000
diff --git a/main/searchindex.js b/main/searchindex.js
index 53b6bc313..462993dbe 100644
--- a/main/searchindex.js
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