Benchmarks for Spinning Up Implementations

We benchmarked the Spinning Up algorithm implementations in five environments from the MuJoCo Gym task suite: HalfCheetah, Hopper, Walker2d, Swimmer, and Ant.

Performance in Each Environment

HalfCheetah

../_images/bench_halfcheetah.svg

3M timestep benchmark for HalfCheetah-2.

Hopper

../_images/bench_hopper.svg

3M timestep benchmark for Hopper-v2.

Walker

../_images/bench_walker.svg

3M timestep benchmark for Walker2d-v2.

Swimmer

../_images/bench_swim.svg

3M timestep benchmark for Swimmer-v2.

Ant

../_images/bench_ant.svg

3M timestep benchmark for Ant-v2.

Experiment Details

Random seeds. The on-policy algorithms (VPG, TPRO, PPO) were run for 3 random seeds each, and the off-policy algorithms (DDPG, TD3, SAC) were run for 10 random seeds each. Graphs show the average (solid line) and std dev (shaded) of performance over random seed over the course of training.

Performance metric. Performance for the on-policy algorithms is measured as the average trajectory return across the batch collected at each epoch. Performance for the off-policy algorithms is measured once every 10,000 steps by running the deterministic policy (or, in the case of SAC, the mean policy) without action noise for ten trajectories, and reporting the average return over those test trajectories.

Network architectures. The on-policy algorithms use networks of size (64, 32) with tanh units for both the policy and the value function. The off-policy algorithms use networks of size (400, 300) with relu units.

Batch size. The on-policy algorithms collected 4000 steps of agent-environment interaction per batch update. The off-policy algorithms used minibatches of size 100 at each gradient descent step.

All other hyperparameters are left at default settings for the Spinning Up implementations. See algorithm pages for details.