from pybrain.rl.agents import LearningAgent from pybrain.rl.learners.valuebased import NFQ, ActionValueNetwork from pybrain.rl.experiments import EpisodicExperiment from environment import Environment from tasks import BalanceTask from training import NFQTraining task = BalanceTask() task.performAction(1) print task.getObservation() task.performAction(2) print task.getObservation() task.performAction(3) print task.getObservation()
from pybrain.rl.agents import LearningAgent
from pybrain.rl.learners.valuebased import NFQ, ActionValueNetwork
from pybrain.rl.experiments import EpisodicExperiment
from environment import Environment
from tasks import BalanceTask
from training import NFQTraining
task = BalanceTask(only_steer=True)
action_value_function = ActionValueNetwork(task.outdim, task.nactions,
name='BalanceNFQActionValueNetwork')
learner = NFQ()
#learner.gamma = 0.99
#learner.explorer.epsilon = 0.5
task.discount = learner.gamma
agent = LearningAgent(action_value_function, learner)
performance_agent = LearningAgent(action_value_function, None)
experiment = EpisodicExperiment(task, agent)
tr = NFQTraining('balance_nfq', experiment, performance_agent)
tr.train(7000, performance_interval=1, n_performance_episodes=1, plot_action_history=True)
from pybrain.rl.agents import LearningAgent
from pybrain.rl.learners.valuebased import NFQ, ActionValueNetwork
from pybrain.rl.experiments import EpisodicExperiment
from environment import Environment
from tasks import BalanceTask
from training import NFQTraining
task = BalanceTask()
action_value_function = ActionValueNetwork(task.outdim, task.nactions,
name='BalanceNFQActionValueNetwork')
learner = NFQ()
learner.gamma = 0.9999
learner.explorer.epsilon = 0.9
task.discount = learner.gamma
agent = LearningAgent(action_value_function, learner)
performance_agent = LearningAgent(action_value_function, None)
experiment = EpisodicExperiment(task, agent)
tr = NFQTraining('balance_nfq', experiment, performance_agent)
tr.train(7000, performance_interval=1, n_performance_episodes=1, plotsave_interval=10, plot_action_history=True)
from pybrain.rl.agents import LearningAgent from pybrain.rl.learners.valuebased import NFQ, ActionValueNetwork from pybrain.rl.experiments import EpisodicExperiment from environment import Environment from tasks import BalanceTask from training import NFQTraining task = BalanceTask() task.performAction(1) print task.getObservation() task.performAction(2) print task.getObservation() task.performAction(3) print task.getObservation()
