WebRaw. flappybird-DQN. This is a submission of the second training epoch of Flappy Bird using Deep Q-Learning, built with Python and Tensorflow. The first epoch was trained with the following settings: First buffer with 1000 random iterations. training episodes: 20000. learning rate for Adam Optimizer: 1e-5. WebSep 20, 2024 · Table trainingparameters DQNParameters value Observe steps 100000 Explore steps itialepsilo 0.1Fin al epsil 0.001Replay memory 50000 batch size 32 lear ning rate 0.000001 firsthid FPS 30 optimizati algorithmAdam 3.2 Results Analysis We train our model about epochs.Figure firsthidde biasesfin ally cen tralize around lowvarianee, which …
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Deep Reinforcement Learning for Flappy Bird - Stanford …
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