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  # **Reinforce** Agent playing **LunarLanderContinuous-v2**
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  This is a custom REINFORCE RL agent. Performance has been measured over 900 episodes.
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- To try the agent, user needs to import the ParameterisedPolicy class from the agent_class.py file. </br>
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  Training progress:
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  ![training](training_graph.jpg)
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  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.
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- 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.
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  Minimal code to use the agent:</br>
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  ```
 
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  # **Reinforce** Agent playing **LunarLanderContinuous-v2**
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  This is a custom REINFORCE RL agent. Performance has been measured over 900 episodes.
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+ To try the agent, user needs to import the `ParameterisedPolicy` class from the agent_class.py file. </br>
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  Training progress:
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  ![training](training_graph.jpg)
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  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.
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+ 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.
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  Minimal code to use the agent:</br>
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  ```