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from tensorforce import Configuration
from tensorforce.agents import TRPOAgent
from tensorforce.core.networks import layered_network_builder

config = Configuration(
  batch_size=100,
  state=dict(shape=(10,)),
  actions=dict(continuous=False, num_actions=2),
  network=layered_network_builder([dict(type='dense', size=50), dict(type='dense', size=50)])
)

# Create a Trust Region Policy Optimization agent
agent = TRPOAgent(config=config)

# Get new data from somewhere, e.g. a client to a web app
client = MyClient('http://127.0.0.1', 8080)

# Poll new state from client
state = client.get_state()

# Get prediction from agent, execute
action = agent.act(state=state)
reward = client.execute(action)

# Add experience, agent automatically updates model according to batch size
agent.observe(reward=reward, terminal=False)

More information:
Reinforcement Learning and TensorForce

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