Population Based Training (in PyTorch with sqlite3). Status: Unsupported
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Updated
Jan 31, 2018 - Python
Population Based Training (in PyTorch with sqlite3). Status: Unsupported
A simple PyTorch implementation of Population Based Training of Neural Networks.
Population-Based Training (PBT) for Reinforcement Learning using Message Passing Interface (MPI)
Reproducing results from DeepMind's paper on Population Based Training of Neural Networks.
Jupyter notebooks to play around with population based training, as described in https://arxiv.org/abs/1711.09846
Vectorization techniques for fast population-based training.
🥕 Mastering flappy bird with machine learning (neural networks, neuro-evolution)
Training in bursts for defending against adversarial policies
Implementation of the Google DeepMind paper introducing population-based training, except applied to simulated annealing instead of neural networks.
Applying Population Based Training on Generative Adversarial Networks.
Article 1 code with bonus Population Based Training support
A Population Based Reinforcement Learning Library based on PyTorch
Implementation of RPPO(Risk-sensitive PPO) and RPBT(Population-based self-play with RPPO)
Curriculum training a Differentiable Neural Computer using Population Based Training
Population Based Training, Figure 2
My attempt to reproduce a water down version of PBT (Population based training) for MARL (Multi-agent reinforcement learning) using DDPPO (Decentralized & distributed proximal policy optimization) from ray[rllib].
Population-Based Training (PBT) implementation on ddpg
Optimize for topology, hyperparameters, and weights of Neural Nets in single joint training process using Augmented Population Based Training
Implementation of the basic example of PBT in Rust from https://arxiv.org/abs/1711.09846
Generating Evolutionary Opponents as a Reinforcement Guided Exploration Solution
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