hydra:
  run:
    dir: outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}_${hydra.job.name}
  job:
    name: default

seed: 1337
device: cuda
buffer_device: cuda
eval_freq: 1000
save_freq: 10000
eval_episodes: 20
save_video: false
save_model: false
save_buffer: false

# env
env: simxarm
task: lift
from_pixels: True
pixels_only: False
image_size: 84
fps: 15

reward_scale: 1.0


# xarm_lift
episode_length: 25
modality: 'all'
action_repeat: 2 # TODO(rcadene): verify we use this
discount: 0.9
train_steps: 50000

# pixels
frame_stack: 1
num_channels: 32
img_size: ${image_size}
state_dim: 4
action_dim: 4

# TDMPC
policy: tdmpc

# planning
mpc: true
iterations: 6
num_samples: 512
num_elites: 50
mixture_coef: 0.1
min_std: 0.05
max_std: 2.0
temperature: 0.5
momentum: 0.1
uncertainty_cost: 1

# actor
log_std_min: -10
log_std_max: 2

# learning
batch_size: 256
max_buffer_size: 10000
horizon: 5
reward_coef: 0.5
value_coef: 0.1
consistency_coef: 20
rho: 0.5
kappa: 0.1
lr: 3e-4
std_schedule: ${min_std}
horizon_schedule: ${horizon}
per: true
per_alpha: 0.6
per_beta: 0.4
grad_clip_norm: 10
seed_steps: 0
update_freq: 2
tau: 0.01
utd: 1

# offline rl
# dataset_dir: ???
data_first_percent: 1.0
is_data_clip: true
data_clip_eps: 1e-5
expectile: 0.9
A_scaling: 3.0

# offline->online
offline_steps: 25000 # ${train_steps}/2
pretrained_model_path: ""
# pretrained_model_path: "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/offline.pt"
# pretrained_model_path: "/home/rcadene/code/fowm/logs/xarm_lift/all/default/2/models/final.pt"
balanced_sampling: true
demo_schedule: 0.5

# architecture
enc_dim: 256
num_q: 5
mlp_dim: 512
latent_dim: 50

# wandb
use_wandb: true
wandb_project: lerobot
wandb_entity: rcadene  # insert your own
notes: ""