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/develop/bisim-sac-transfer
yanchaosun 4 年前
当前提交
de4870be
共有 4 个文件被更改,包括 200 次插入0 次删除
  1. 50
      config/sac_transfer/3DBallHardTransferCloud1.yaml
  2. 50
      config/sac_transfer/3DBallHardTransferCloud2.yaml
  3. 50
      config/sac_transfer/3DBallHardTransferCloud3.yaml
  4. 50
      config/sac_transfer/3DBallHardTransferCloud4.yaml

50
config/sac_transfer/3DBallHardTransferCloud1.yaml


behaviors:
3DBallHard:
trainer_type: sac_transfer
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
batch_size: 256
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 1.0
reward_signal_steps_per_update: 10.0
encoder_layers: 2
policy_layers: 0
forward_layers: 0
value_layers: 1
feature_size: 64
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: false
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: true
train_model: false
load_policy: true
load_value: true
transfer_path: "results/ball-linear-s1/3DBall"
network_settings:
normalize: true
hidden_units: 128
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 1000
summary_freq: 12000
threaded: true

50
config/sac_transfer/3DBallHardTransferCloud2.yaml


behaviors:
3DBallHard:
trainer_type: sac_transfer
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
batch_size: 256
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 1.0
reward_signal_steps_per_update: 10.0
encoder_layers: 2
policy_layers: 0
forward_layers: 0
value_layers: 1
feature_size: 64
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: false
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: true
train_model: false
load_policy: true
load_value: true
transfer_path: "results/ball-linear-s2/3DBall"
network_settings:
normalize: true
hidden_units: 128
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 1000
summary_freq: 12000
threaded: true

50
config/sac_transfer/3DBallHardTransferCloud3.yaml


behaviors:
3DBallHard:
trainer_type: sac_transfer
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
batch_size: 256
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 1.0
reward_signal_steps_per_update: 10.0
encoder_layers: 2
policy_layers: 0
forward_layers: 0
value_layers: 1
feature_size: 64
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: false
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: true
train_model: false
load_policy: true
load_value: true
transfer_path: "results/ball-linear-s3/3DBall"
network_settings:
normalize: true
hidden_units: 128
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 1000
summary_freq: 12000
threaded: true

50
config/sac_transfer/3DBallHardTransferCloud4.yaml


behaviors:
3DBallHard:
trainer_type: sac_transfer
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
batch_size: 256
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 1.0
reward_signal_steps_per_update: 10.0
encoder_layers: 2
policy_layers: 0
forward_layers: 0
value_layers: 1
feature_size: 64
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: false
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: true
train_model: false
load_policy: true
load_value: true
transfer_path: "results/ball-linear-s4/3DBall"
network_settings:
normalize: true
hidden_units: 128
num_layers: 2
vis_encode_type: simple
reward_signals:
extrinsic:
gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
time_horizon: 1000
summary_freq: 12000
threaded: true
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