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spinning-up-basic

Basic versions of agents from Spinning Up in Deep RL written in PyTorch. Designed to run quickly on CPU on Pendulum-v0 from OpenAI Gym.

To see differences between algorithms, try running diff -y <file1> <file2>, e.g., diff -y ddpg.py td3.py.

For MPI versions of on-policy algorithms, see the mpi branch.

Algorithms

Implementation Details

Note that implementation details can have a significant effect on performance, as discussed in What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study. This codebase attempts to be as simple as possible, but note that for instance on-policy algorithms use separate actor and critic networks, a state-independent policy standard deviation, per-minibatch advantage normalisation, and several critic updates per minibatch, while the deterministic off-policy algorithms use layer normalisation. Equally, soft actor-critic uses a transformed Normal distribution by default, but this can also help the on-policy algorithms.

Results

Vanilla Policy Gradient/Advantage Actor-Critic

VPG

Trust Region Policy Gradient

TRPO

Proximal Policy Optimization

PPO

Deep Deterministic Policy Gradient

DDPG

Twin Delayed DDPG

TD3

Soft Actor-Critic

SAC

Deep Q-Network

DQN

Code Links

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Basic versions of agents from Spinning Up in Deep RL written in PyTorch

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