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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.
All of my reinforcement learning projects (Some of the projects may contain errors :D )
PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.
Automated analysis of hyperparameter configurations for n-step Q-Learning reinforcement agents