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BY571 / IQN-and-Extensions

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PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER, Noisy layer, N-step bootstrapping, Dueling architecture and parallel env support.

reinforcement-learning parallel-computing rainbow pytorch multi-environment dqn reinforcement-learning-algorithms parallel-processing iqn prioritized-experience-replay pytorch-implementation implicit-quantile-networks n-step-bootstrapping distributional-r noisy-layer munchausen
  • Updated Mar 4, 2023
  • Jupyter Notebook

BY571 / FQF-and-Extensions

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PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.

reinforcement-learning multiprocessing pytorch multi-environment reinforcement-learning-algorithms parallel-processing dueling-network-architecture prioritized-experience-replay quantile-functions dqn-pytorch distributional-rl noisy-layer fqf nstep-bootstrapping
  • Updated Oct 10, 2020
  • Jupyter Notebook

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