# Example W&B sweep configuration. Customize the task, parameters, and
# search space for your own experiment.
#
# Usage:
#   ./scripts/cloud/sweep-launch.sh A100:4

name: velocity-lr-entropy-sweep
project: mjlab
program: train
method: random
metric:
  name: Train/mean_reward
  goal: maximize

parameters:
  agent.algorithm.learning-rate:
    distribution: log_uniform_values
    min: 1e-4
    max: 1e-2
  agent.algorithm.entropy-coef:
    distribution: log_uniform_values
    min: 0.001
    max: 0.1

command:
  - ${env}
  - uv
  - run
  - ${program}
  - Mjlab-Velocity-Flat-Unitree-G1
  - --env.scene.num-envs
  - "4096"
  - --agent.max-iterations
  - "6000"
  - ${args}

run_cap: 8