forked from tangger/lerobot
2023289ce8
Added functions for converting the replay buffer from and to LeRobotDataset. When we want to save the replay buffer, we convert it first to LeRobotDataset format and save it locally and vice-versa. Co-authored-by: Adil Zouitine <adilzouitinegm@gmail.com>
100 lines
2.4 KiB
YAML
100 lines
2.4 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/hil-serl-maniskill-pushcube
|
|
|
|
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: 500
|
|
save_freq: 1000000
|
|
|
|
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: 1000000
|
|
online_buffer_seed_size: 0
|
|
online_step_before_learning: 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
|
|
input_shapes:
|
|
# # TODO(rcadene, alexander-soare): add variables for height and width from the dataset/env?
|
|
observation.state: ["${env.state_dim}"]
|
|
observation.image: [3, 64, 64]
|
|
output_shapes:
|
|
action: ["${env.action_dim}"]
|
|
|
|
# Normalization / Unnormalization
|
|
input_normalization_modes: null
|
|
output_normalization_modes:
|
|
action: min_max
|
|
output_normalization_params:
|
|
action:
|
|
min: [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0, -1.0]
|
|
max: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
|
|
|
# Architecture / modeling.
|
|
# Neural networks.
|
|
image_encoder_hidden_dim: 32
|
|
# discount: 0.99
|
|
discount: 0.80
|
|
temperature_init: 1.0
|
|
num_critics: 2 #10
|
|
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:
|
|
actor_ip: "127.0.0.1"
|
|
port: 50051
|
|
|
|
# # 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
|