# Custom Main Configuration
#
# Use with:
#   uv run python scripts/train.py --config-name main_config_custom
#
# This keeps the project defaults intact while giving you a single custom
# training entrypoint you can edit freely.

defaults:
  - brittle_star_config
  - experiment: base
  - logging: default
  - evaluation: default
  - ppo: default
  - architecture: decentralized
  - morphology: 5_arms_full
  - arena: default
  - environment: directed_locomotion
  - simulation: default
  - _self_

architecture:
  topology_type: "fully_connected"

morphology:
  morph_mode: FULLY_CONNECTED

experiment:
  exp_name: "final-models/fully-connected/"
  seed: 42
  torch_deterministic: true
  cuda: true

logging:
  track: true
  save_model: true
  save_checkpoints: true
  upload_final_model: true
  upload_checkpoints: true
  checkpoint_frequency: 20
  wandb_project_name: "final-models"

evaluation:
  evaluate_checkpoints: true
  eval_max_steps: 2000
  eval_seed: 0

ppo:
  learning_rate: 0.0001
  total_timesteps: 16384000
  num_envs: 128
  num_steps: 64
  anneal_lr: true
  gamma: 0.99
  gae_lambda: 0.95
  num_minibatches: 32
  update_epochs: 4
  norm_adv: true
  clip_coef: 0.2
  clip_vloss: true
  ent_coef: 0.001
  vf_coef: 1.0
  max_grad_norm: 0.5
  target_kl: 0.02

environment:
  simulation_time: 100000.0
  target_distance: 3.0

hydra:
  job:
    chdir: true
  run:
    dir: ${experiment.base_run_dir}/${experiment.exp_name}/${now:%Y-%m-%d}/${now:%H-%M-%S}