Gist 7814a13dbae9fde1771606fe6081513d

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Gist 7814a13dbae9fde1771606fe6081513d

✓ Published0🌍 Public

Last edited Jun 2, 2016

Created on May 30, 2016

This interactive visualization tracks the training progress of a Dueling Double Deep Q-Network (Duel DDQN) agent within the OpenAI Gym CartPole-v0 environment. It plots the reward per episode over time, showing how the agent's performance improves as it learns to balance the pole. The code uses the Python libraries `gym` for the environment and `torch` for the neural network, with command-line arguments specifying hyperparameters like `--gamma` and `--batch_size`. The chart updates in real time, rendering the learning curve as the agent iterates through episodes.

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