Topic: "nstep-bootstrapping"
BY571/FQF-and-Extensions
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.
Language: Jupyter Notebook - Size: 2.82 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 24 - Forks: 10

BY571/D4PG
PyTorch implementation of D4PG with the SOTA IQN Critic instead of C51. Implementation includes also the extensions Munchausen RL and D2RL which can be added to D4PG to improve its performance.
Language: Python - Size: 2.17 MB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 13 - Forks: 4

OneUpWallStreet/Reinforcement-Learning
All of my reinforcement learning projects (Some of the projects may contain errors :D )
Language: Jupyter Notebook - Size: 1.53 MB - Last synced at: about 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 0

BY571/TD3-and-Extensions
PyTorch implementation of Twin Delayed Deep Deterministic Policy Gradient (TD3) - including additional Extension to improve the algorithm's performance.
Language: Python - Size: 1.73 MB - Last synced at: about 1 year ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0

frederikgramkortegaard/hyper-q
Automated analysis of hyperparameter configurations for n-step Q-Learning reinforcement agents
Language: Python - Size: 521 KB - Last synced at: 8 days ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0
