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sb3_contrib.ars

ARS

Augmented Random Search (ARS) is a simple reinforcement algorithm that uses a direct random search over policy parameters. It can be surprisingly effective compared to more sophisticated algorithms. In the original paper the authors showed that linear policies trained with ARS were competitive with deep reinforcement learning for the MuJuCo locomotion tasks.

SB3s implementation allows for linear policies without bias or squashing function, it also allows for training MLP policies, which include linear policies with bias and squashing functions as a special case.

Normally one wants to train ARS with several seeds to properly evaluate.

Warning

ARS multi-processing is different from the classic Stable-Baselines3 multi-processing: it runs n environments in parallel but asynchronously. This asynchronous multi-processing is considered experimental and does not fully support callbacks: the on_step() event is called artificially after the evaluation episodes are over.

Available Policies

LinearPolicy MlpPolicy

Notes

Can I use?

  • Recurrent policies: ❌
  • Multi processing: ✔️ (cf. example)
  • Gym spaces:
Space Action Observation
Discrete ✔️

✔️
Box ✔️

✔️
MultiDiscrete ❌

✔️
MultiBinary ❌

✔️
Dict ❌

❌

Example

from sb3_contrib import ARS

# Policy can be LinearPolicy or MlpPolicy
model = ARS("LinearPolicy", "Pendulum-v1", verbose=1)
model.learn(total_timesteps=10000, log_interval=4)
model.save("ars_pendulum")

With experimental asynchronous multi-processing:

from sb3_contrib import ARS
from sb3_contrib.common.vec_env import AsyncEval

from stable_baselines3.common.env_util import make_vec_env

env_id = "CartPole-v1"
n_envs = 2

model = ARS("LinearPolicy", env_id, n_delta=2, n_top=1, verbose=1)
# Create env for asynchronous evaluation (run in different processes)
async_eval = AsyncEval([lambda: make_vec_env(env_id) for _ in range(n_envs)], model.policy)

model.learn(total_timesteps=200_000, log_interval=4, async_eval=async_eval)

Results

Replicating results from the original paper, which used the Mujoco benchmarks. Same parameters from the original paper, using 8 seeds.

Environments ARS
HalfCheetah 4398 +/- 320
Swimmer 241 +/- 51
Hopper 3320 +/- 120

How to replicate the results?

Clone RL-Zoo and checkout the branch feat/ars

git clone https://github.com/DLR-RM/rl-baselines3-zoo
cd rl-baselines3-zoo/
git checkout feat/ars

Run the benchmark. The following code snippet trains 8 seeds in parallel

for ENV_ID in Swimmer-v3 HalfCheetah-v3 Hopper-v3
do
    for SEED_NUM in {1..8}
    do
        SEED=$RANDOM
        python train.py --algo ars --env $ENV_ID --eval-episodes 10 --eval-freq 10000 -n 20000000 --seed  $SEED &
        sleep 1
    done
    wait
done

Plot the results:

python scripts/all_plots.py -a ars -e HalfCheetah Swimmer Hopper -f logs/ -o logs/ars_results -max 20000000
python scripts/plot_from_file.py -i logs/ars_results.pkl -l ARS

Parameters

ARS

ARS Policies

sb3_contrib.ars.policies.ARSPolicy

LinearPolicy

MlpPolicy