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Algorithms — Spinning Up documentation

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They are all implemented with MLP (non-recurrent) actor-critics, making them suitable for fully-observed, non-image-based RL environments, e.g. the Gym Mujoco environments. Spinning Up has two implementations for each algorithm (except for TRPO): one that uses PyTorch as the neural network library, and one that uses Tensorflow v1 as the neural network library. (TRPO is currently only available in Tensorflow.) We chose the core deep RL algorithms in this package to reflect useful progressions of ideas from the recent history of the field, culminating in two algorithms in particular—PPO and SAC—which are close to state of the art on reliability and sample efficiency among policy-learning algorithms. They also expose some of the trade-offs that get made in designing and using algorithms in deep RL. Vanilla Policy Gradient is the most basic, entry-level algorithm in the deep RL space because it completely predates the advent of deep RL altogether. The core elements of VPG go all the way ba

Algorithms - Spinning Up documentation --> Docs >> Algorithms Edit on GitHub Algorithms ¶ Table of Contents Algorithms What’s Included Why These Algorithms? The On-Policy Algorithms The Off-Policy Algorithms Code Format The Algorithm Function: PyTorch Version The Algorithm Function: Tensorflow Version The Core File What’s Included ¶ The following algorithms are implemented in the Spinning Up package: Vanilla Policy Gradient (VPG) Trust Region Policy Optimization (TRPO) Proximal Policy Optimization (PPO) Deep Deterministic Policy Gradient (DDPG) Twin Delayed DDPG (TD3) Soft Actor-Cr

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