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# 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