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Deep-Reinforcement-Learning-Algorithms
32 projects in the framework of Deep Reinforcement Learning algorithms: Q-learning, DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.
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You can find the solution for MountainCar env here: https://github.com/Rafael1s/Deep-Reinforcement-Learning-Algorithms/tree/master/MountainCarContinuous-TD3This solution implemented using PyTorch. The TD3 model is the successor to DDPG algorithm using the Actor-Critic model.
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Is there a canonical simple "helloworld" neural network design? Something beyond AND/OR logic, a handful of nodes that does something mildly "useful"?
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Training time of CartPole is way to long
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Is it better to not use the Target Update Frequency in Double DQN or depends on the application?
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Working with DQN ! need some help !
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他們能回來嗎