PPO-for-Beginners
pytorch-learn-reinforcement-learning
PPO-for-Beginners | pytorch-learn-reinforcement-learning | |
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1 | 3 | |
653 | 143 | |
- | - | |
4.2 | 0.0 | |
5 months ago | about 3 years ago | |
Python | Python | |
MIT License | MIT License |
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PPO-for-Beginners
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Why does this PPO implementation calculate the Advantage only once per rollout?
I am looking at this PPO implementation, which follows the pseudocode given in Spinning Up. This implementation has been really easy to follow and I understand almost everything. However, I am lost in line 103, where the author computes the normalized advantage before the rollout -
pytorch-learn-reinforcement-learning
What are some alternatives?
stable-baselines3 - PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Super-mario-bros-PPO-pytorch - Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
PPO-PyTorch - Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Tetris-deep-Q-learning-pytorch - Deep Q-learning for playing tetris game
R-NaD - Experimentation with Regularized Nash Dynamics on a GPU accelerated game
tianshou - An elegant PyTorch deep reinforcement learning library.
cleanrl - High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
6DRepNet - Official Pytorch implementation of 6DRepNet: 6D Rotation representation for unconstrained head pose estimation.
stable-baselines3-contrib - Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code
Amortized-SVGD-GAN - Learning to draw samples: with application to amortized maximum likelihood estimator for generative adversarial learning
nes-torch - Minimal PyTorch Library for Natural Evolution Strategies
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!