121 lines
3.3 KiB
YAML
121 lines
3.3 KiB
YAML
# @package _global_
|
|
|
|
# Train with:
|
|
#
|
|
# python lerobot/scripts/train.py \
|
|
# +dataset=lerobot/pusht_keypoints
|
|
# env=pusht \
|
|
# env.gym.obs_type=environment_state_agent_pos \
|
|
|
|
seed: 1
|
|
dataset_repo_id: aractingi/insertcube_simple
|
|
|
|
training:
|
|
# Offline training dataloader
|
|
num_workers: 4
|
|
|
|
# batch_size: 256
|
|
batch_size: 512
|
|
grad_clip_norm: 10.0
|
|
lr: 3e-4
|
|
|
|
eval_freq: 2500
|
|
log_freq: 1
|
|
save_freq: 2000000
|
|
|
|
online_steps: 1000000
|
|
online_rollout_n_episodes: 10
|
|
online_rollout_batch_size: 10
|
|
online_steps_between_rollouts: 1000
|
|
online_sampling_ratio: 1.0
|
|
online_env_seed: 10000
|
|
online_buffer_capacity: 10000
|
|
online_buffer_seed_size: 0
|
|
online_step_before_learning: 100 #5000
|
|
do_online_rollout_async: false
|
|
policy_update_freq: 1
|
|
|
|
# delta_timestamps:
|
|
# observation.environment_state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
|
|
# observation.state: "[i / ${fps} for i in range(${policy.horizon} + 1)]"
|
|
# action: "[i / ${fps} for i in range(${policy.horizon})]"
|
|
# next.reward: "[i / ${fps} for i in range(${policy.horizon})]"
|
|
|
|
policy:
|
|
name: sac
|
|
|
|
pretrained_model_path:
|
|
|
|
# Input / output structure.
|
|
n_action_repeats: 1
|
|
horizon: 1
|
|
n_action_steps: 1
|
|
|
|
shared_encoder: true
|
|
vision_encoder_name: "helper2424/resnet10"
|
|
freeze_vision_encoder: true
|
|
input_shapes:
|
|
# # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
|
|
observation.state: ["${env.state_dim}"]
|
|
observation.images.front: [3, 128, 128]
|
|
observation.images.side: [3, 128, 128]
|
|
# observation.image: [3, 128, 128]
|
|
output_shapes:
|
|
action: ["${env.action_dim}"]
|
|
|
|
# Normalization / Unnormalization
|
|
input_normalization_modes:
|
|
observation.images.front: mean_std
|
|
observation.images.side: mean_std
|
|
observation.state: min_max
|
|
input_normalization_params:
|
|
observation.images.front:
|
|
mean: [0.485, 0.456, 0.406]
|
|
std: [0.229, 0.224, 0.225]
|
|
observation.images.side:
|
|
mean: [0.485, 0.456, 0.406]
|
|
std: [0.229, 0.224, 0.225]
|
|
observation.state:
|
|
# 6- joint positions, 6- joint velocities, 3- ee position
|
|
max: [ 52.822266, 136.14258, 142.03125, 72.1582, 22.675781, -0.5673759, 100., 100., 100., 100., 100., 100., 0.25295413, 0.07498981, 0.06862044]
|
|
min: [-2.6367188, 86.572266, 89.82422, 12.392578, -26.015625, -0.5673759, -100., -100., -100., -100., -100., -100., 0.2010096, -0.12, 0.0433196]
|
|
|
|
output_normalization_modes:
|
|
action: min_max
|
|
output_normalization_params:
|
|
action:
|
|
min: [-0.03, -0.03, -0.01]
|
|
max: [0.03, 0.03, 0.03]
|
|
|
|
# Architecture / modeling.
|
|
# Neural networks.
|
|
image_encoder_hidden_dim: 32
|
|
# discount: 0.99
|
|
discount: 0.97
|
|
temperature_init: 1.0
|
|
num_critics: 2 #10
|
|
camera_number: 2
|
|
num_subsample_critics: null
|
|
critic_lr: 3e-4
|
|
actor_lr: 3e-4
|
|
temperature_lr: 3e-4
|
|
# critic_target_update_weight: 0.005
|
|
critic_target_update_weight: 0.01
|
|
utd_ratio: 2 # 10
|
|
|
|
actor_learner_config:
|
|
learner_host: "127.0.0.1"
|
|
learner_port: 50051
|
|
policy_parameters_push_frequency: 15
|
|
|
|
# # Loss coefficients.
|
|
# reward_coeff: 0.5
|
|
# expectile_weight: 0.9
|
|
# value_coeff: 0.1
|
|
# consistency_coeff: 20.0
|
|
# advantage_scaling: 3.0
|
|
# pi_coeff: 0.5
|
|
# temporal_decay_coeff: 0.5
|
|
# # Target model.
|
|
# target_model_momentum: 0.995
|