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
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
Pyramids:
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
pretraining:
demo_path: ./demos/ExpertPyramid.demo
strength: 0.5
steps: 10000
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
curiosity:
strength: 0.02
gamma: 0.99
encoding_size: 256
gail:
strength: 0.01
gamma: 0.99
encoding_size: 128
demo_path: demos/ExpertPyramid.demo
CrawlerStatic:
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:
gail:
strength: 1.0
gamma: 0.99
encoding_size: 128
demo_path: demos/ExpertCrawlerSta.demo
PushBlock:
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
reward_signals:
gail:
strength: 1.0
gamma: 0.99
encoding_size: 128
demo_path: demos/ExpertPush.demo
Hallway:
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
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
gail:
strength: 0.1
gamma: 0.99
encoding_size: 128
demo_path: demos/ExpertHallway.demo