Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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default:
trainer: ppo
batch_size: 1024
beta: 5.0e-3
buffer_size: 10240
epsilon: 0.2
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
memory_size: 256
normalize: false
num_epoch: 3
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
vis_encode_type: simple
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
BananaLearning:
normalize: false
beta: 5.0e-3
batch_size: 1024
buffer_size: 10240
max_steps: 1.0e5
BouncerLearning:
normalize: true
max_steps: 5.0e5
num_layers: 2
hidden_units: 64
PushBlockLearning:
max_steps: 5.0e4
batch_size: 128
buffer_size: 2048
beta: 1.0e-2
hidden_units: 256
summary_freq: 2000
time_horizon: 64
num_layers: 2
SmallWallJumpLearning:
max_steps: 1.0e6
batch_size: 128
buffer_size: 2048
beta: 5.0e-3
hidden_units: 256
summary_freq: 2000
time_horizon: 128
num_layers: 2
normalize: false
BigWallJumpLearning:
max_steps: 1.0e6
batch_size: 128
buffer_size: 2048
beta: 5.0e-3
hidden_units: 256
summary_freq: 2000
time_horizon: 128
num_layers: 2
normalize: false
StrikerLearning:
max_steps: 5.0e5
learning_rate: 1e-3
batch_size: 128
num_epoch: 3
buffer_size: 2000
beta: 1.0e-2
hidden_units: 256
summary_freq: 2000
time_horizon: 128
num_layers: 2
normalize: false
GoalieLearning:
max_steps: 5.0e5
learning_rate: 1e-3
batch_size: 320
num_epoch: 3
buffer_size: 2000
beta: 1.0e-2
hidden_units: 256
summary_freq: 2000
time_horizon: 128
num_layers: 2
normalize: false
PyramidsLearning:
summary_freq: 2000
time_horizon: 128
batch_size: 128
buffer_size: 2048
hidden_units: 512
num_layers: 2
beta: 1.0e-2
max_steps: 5.0e5
num_epoch: 3
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
curiosity:
strength: 0.02
gamma: 0.99
encoding_size: 256
VisualPyramidsLearning:
time_horizon: 128
batch_size: 64
buffer_size: 2024
hidden_units: 256
num_layers: 1
beta: 1.0e-2
max_steps: 5.0e5
num_epoch: 3
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
curiosity:
strength: 0.01
gamma: 0.99
encoding_size: 256
3DBallLearning:
normalize: true
batch_size: 64
buffer_size: 12000
summary_freq: 1000
time_horizon: 1000
lambd: 0.99
beta: 0.001
3DBallHardLearning:
normalize: true
batch_size: 1200
buffer_size: 12000
summary_freq: 1000
time_horizon: 1000
max_steps: 5.0e5
beta: 0.001
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.995
TennisLearning:
normalize: true
max_steps: 2e5
CrawlerStaticLearning:
normalize: true
num_epoch: 3
time_horizon: 1000
batch_size: 2024
buffer_size: 20240
max_steps: 1e6
summary_freq: 3000
num_layers: 3
hidden_units: 512
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.995
CrawlerDynamicLearning:
normalize: true
num_epoch: 3
time_horizon: 1000
batch_size: 2024
buffer_size: 20240
max_steps: 1e6
summary_freq: 3000
num_layers: 3
hidden_units: 512
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.995
WalkerLearning:
normalize: true
num_epoch: 3
time_horizon: 1000
batch_size: 2048
buffer_size: 20480
max_steps: 2e6
summary_freq: 3000
num_layers: 3
hidden_units: 512
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.995
ReacherLearning:
normalize: true
num_epoch: 3
time_horizon: 1000
batch_size: 2024
buffer_size: 20240
max_steps: 1e6
summary_freq: 3000
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.995
HallwayLearning:
use_recurrent: true
sequence_length: 64
num_layers: 2
hidden_units: 128
memory_size: 256
beta: 1.0e-2
num_epoch: 3
buffer_size: 1024
batch_size: 128
max_steps: 5.0e5
summary_freq: 1000
time_horizon: 64
VisualHallwayLearning:
use_recurrent: true
sequence_length: 64
num_layers: 1
hidden_units: 128
memory_size: 256
beta: 1.0e-2
num_epoch: 3
buffer_size: 1024
batch_size: 64
max_steps: 5.0e5
summary_freq: 1000
time_horizon: 64
VisualPushBlockLearning:
use_recurrent: true
sequence_length: 32
num_layers: 1
hidden_units: 128
memory_size: 256
beta: 1.0e-2
num_epoch: 3
buffer_size: 1024
batch_size: 64
max_steps: 5.0e5
summary_freq: 1000
time_horizon: 64
GridWorldLearning:
batch_size: 32
normalize: false
num_layers: 1
hidden_units: 256
beta: 5.0e-3
buffer_size: 256
max_steps: 5.0e5
summary_freq: 2000
time_horizon: 5
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.9
BasicLearning:
batch_size: 32
normalize: false
num_layers: 1
hidden_units: 20
beta: 5.0e-3
buffer_size: 256
max_steps: 5.0e5
summary_freq: 2000
time_horizon: 3
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.9