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REINFORCE-LunarLanderContinuous-v2

Reinforcement Learning

LunarLanderContinuous-v2

custom-implementation

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igorcheb commited on Feb 2, 2023

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@@ -29,7 +29,7 @@ Training progress:
 Numbers on X axis are average over 40 episodes, each lasting for about 500 timesteps on average. So in total the agent was trained over about 5e6 timesteps.
 Learning rate decay schedule: <code>torch.optim.lr_scheduler.StepLR(opt, step_size=4000, gamma=0.7)</code>. Training code is shown in the training.py file for reference.
-In case video snippet does not work, here's a gif:
 ![replay](replay.gif)
 Minimal code to use the agent:</br>

 Numbers on X axis are average over 40 episodes, each lasting for about 500 timesteps on average. So in total the agent was trained over about 5e6 timesteps.
 Learning rate decay schedule: <code>torch.optim.lr_scheduler.StepLR(opt, step_size=4000, gamma=0.7)</code>. Training code is shown in the training.py file for reference.
+In case video demo does not work, here's a gif:
 ![replay](replay.gif)
 Minimal code to use the agent:</br>