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3dball mass=5 transfer test

/develop/bisim-sac-transfer
yanchaosun 4 年前
当前提交
86830ac9
共有 8 个文件被更改,包括 391 次插入1 次删除
  1. 2
      config/sac_transfer/3DBall.yaml
  2. 35
      config/sac/3DBallMass.yaml
  3. 55
      config/sac_transfer/3DBallMassCloud.yaml
  4. 60
      config/sac_transfer/3DBallMassTransferCloud.yaml
  5. 60
      config/sac_transfer/3DBallMassTransferCloud1.yaml
  6. 60
      config/sac_transfer/3DBallMassTransferCloud2.yaml
  7. 60
      config/sac_transfer/3DBallMassTransferCloud3.yaml
  8. 60
      config/sac_transfer/3DBallMassTransferCloud4.yaml

2
config/sac_transfer/3DBall.yaml


gamma: 0.99
strength: 1.0
keep_checkpoints: 5
max_steps: 500000
max_steps: 1000000
time_horizon: 1000
summary_freq: 12000
threaded: true

35
config/sac/3DBallMass.yaml


behaviors:
3DBall:
trainer_type: sac
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: constant
batch_size: 64
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 0.5
reward_signal_steps_per_update: 10.0
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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

55
config/sac_transfer/3DBallMassCloud.yaml


behaviors:
3DBall:
trainer_type: sac_transfer
hyperparameters:
learning_rate: 0.0003
learning_rate_schedule: linear
model_schedule: linear
batch_size: 64
buffer_size: 500000
buffer_init_steps: 0
tau: 0.005
steps_per_update: 10.0
save_replay_buffer: false
init_entcoef: 0.5
reward_signal_steps_per_update: 10.0
encoder_layers: 2
policy_layers: 0
forward_layers: 0
value_layers: 1
feature_size: 64
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
separate_model_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

60
config/sac_transfer/3DBallMassTransferCloud.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
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: false
load_encoder: true
train_encoder: false
load_action: true
train_action: false
transfer_path: "results/ball-s0/3DBall"
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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

60
config/sac_transfer/3DBallMassTransferCloud1.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
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: false
load_encoder: true
train_encoder: false
load_action: true
train_action: false
transfer_path: "results/ball-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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

60
config/sac_transfer/3DBallMassTransferCloud2.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
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: false
load_encoder: true
train_encoder: false
load_action: true
train_action: false
transfer_path: "results/ball-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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

60
config/sac_transfer/3DBallMassTransferCloud3.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
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: false
load_encoder: true
train_encoder: false
load_action: true
train_action: false
transfer_path: "results/ball-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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5

60
config/sac_transfer/3DBallMassTransferCloud4.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
action_layers: 1
action_feature_size: 32
# separate_value_net: true
separate_policy_train: true
# separate_value_train: true
reuse_encoder: true
in_epoch_alter: false
in_batch_alter: true
use_op_buffer: false
use_var_predict: true
with_prior: false
predict_return: true
use_bisim: false
use_transfer: true
load_model: false
load_encoder: true
train_encoder: false
load_action: true
train_action: false
transfer_path: "results/ball-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
parameter_randomization:
mass:
sampler_type: uniform
sampler_parameters:
min_value: 5
max_value: 5
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