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iramamy Mar 14, 2024
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iramamy Apr 3, 2024
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2 changes: 1 addition & 1 deletion practicals/introduction_to_reinforcement_learning.ipynb
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Expand Up @@ -3113,7 +3113,7 @@
"\n",
"**Next Steps:** \n",
"\n",
"Now that you have successfully solved CartPole with two different RL algorithms, REINFORCE and Deep Q-Learning, we now encourage you to use what you have learnt to try and solve some more challenging environments. OpenAI Gym is a great place to find RL environments. [LunarLander](https://www.gymlibrary.ml/environments/box2d/lunar_lander/) is a great next step. You can go to the start of this notebook ande replace the environment with LunarLander by replacing `env = gym.make(\"CartPole-v1\")` with env = `gym.make(\"LunarLander-v2\")`. Note, you will need to increase the number of training episodes in order to learn a good policy in LunarLander because it is a significantly more challenging environment than CartPole.\n",
"Now that you have successfully solved CartPole with two different RL algorithms, REINFORCE and Deep Q-Learning, we now encourage you to use what you have learnt to try and solve some more challenging environments. OpenAI Gym is a great place to find RL environments. [LunarLander](https://gymnasium.farama.org/environments/box2d/lunar_lander/) is a great next step. You can go to the start of this notebook ande replace the environment with LunarLander by replacing `env = gym.make(\"CartPole-v1\")` with env = `gym.make(\"LunarLander-v2\")`. Note, you will need to increase the number of training episodes in order to learn a good policy in LunarLander because it is a significantly more challenging environment than CartPole.\n",
"\n",
"<center>\n",
"<img src=\"https://miro.medium.com/max/1194/1*Dj2fkRjrMA0w9E-PuyETdg.gif\" width=\"60%\" />\n",
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