Скачать или смотреть Dead Or Alive Deep Reinforcement Learning Agent - Different Training Steps Comparison

Three gameplay clips showing different training stages of our #DeepRL agent while learning to 𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗽𝗹𝗮𝘆 𝗗𝗲𝗮𝗱 𝗢𝗿 𝗔𝗹𝗶𝘃𝗲++ 🤖🎮

𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗱𝗲𝘁𝗮𝗶𝗹𝘀
Discrete 𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝘀𝗽𝗮𝗰𝗲: 12 different actions
𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 𝘀𝗽𝗮𝗰𝗲:
Stack of last four gray-scale pixel frames (128x128x4 px)
Health Bar Values
Side (Left/Right)
Stage number
Stack of previous 12 actions
𝗣𝗿𝗼𝘅𝗶𝗺𝗮𝗹 𝗣𝗼𝗹𝗶𝗰𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Algorithm (https://arxiv.org/pdf/1707.06347.pdf)
𝗗𝗲𝗲𝗽 𝗣𝗼𝗹𝗶𝗰𝘆 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 (shared with value network) architecture:
A) Frames encoder (DQN Nature paper CNN) (https://web.stanford.edu/class/psych2...)
B) Numerical info encoder (2 FC Layers)
C) Latent features concatenation (A+B) + additional FC Layer
⚡𝟴 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀⚡ to collect experience for training
Discount rate: 0.94 (constant through training)
Linear scheduling for learning rate and clipping factor (from 5. e-4 to 5. e-6)

Major upgrades for our #DIAMBRA RL environment are on the way🚀, 𝗯𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝘁𝗼 𝗿𝗼𝗰𝗸!🤜🤛 (Github: https://github.com/diambra/DIAMBRAenv...)

Our 𝗧𝘄𝗶𝘁𝗰𝗵 𝗹𝗶𝘃𝗲𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘀𝘁𝗮𝗿𝘁 𝘀𝗼𝗼𝗻, stay tuned!
GitHub Repo: https://github.com/diambra/diambraArena
Website: https://diambra.ai
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Twitch:   / diambra_ai  
Linkedin:   / diambra