from pybrain.rl.agents.linearfa import LinearFA_Agent from pybrain.rl.experiments import EpisodicExperiment from environment import Environment from tasks import LinearFATileCoding3456BalanceTaskRewardPower8 from training import LinearFATraining from learners import SARSALambda_LinFA_ReplacingTraces task = LinearFATileCoding3456BalanceTaskRewardPower8() learner = SARSALambda_LinFA_ReplacingTraces(task.nactions, task.outdim) learner._lambda = 0.95 task.discount = learner.rewardDiscount agent = LinearFA_Agent(learner) agent.epsilonGreedy = True agent.init_exploration = 0.5 # The state has a huge number of dimensions, and the logging causes me to run # out of memory. We needn't log, since learning is done online. agent.logging = False performance_agent = LinearFA_Agent(learner) performance_agent.logging = False performance_agent.greedy = True performance_agent.learning = False experiment = EpisodicExperiment(task, agent) # TODO PyBrain says that the learning rate needs to decay, but I don't see that # described in Randlov's paper. # A higher number here means the learning rate decays slower. learner.learningRateDecay = 2000 # NOTE increasing this number above from the default of 100 is what got the # learning to actually happen, and fixed the bug/issue where the performance # agent's performance stopped improving.
from pybrain.rl.agents.linearfa import LinearFA_Agent from pybrain.rl.experiments import EpisodicExperiment from environment import Environment from tasks_old import LinearFATileCoding3456BalanceTaskRewardBipolar from training import LinearFATraining from learners import SARSALambda_LinFA_ReplacingTraces task = LinearFATileCoding3456BalanceTaskRewardBipolar() learner = SARSALambda_LinFA_ReplacingTraces(task.nactions, task.outdim) learner._lambda = 0.95 task.discount = learner.rewardDiscount agent = LinearFA_Agent(learner) agent.epsilonGreedy = True agent.init_exploration = 0.5 # The state has a huge number of dimensions, and the logging causes me to run # out of memory. We needn't log, since learning is done online. agent.logging = False performance_agent = LinearFA_Agent(learner) performance_agent.logging = False performance_agent.greedy = True performance_agent.learning = False experiment = EpisodicExperiment(task, agent) # TODO PyBrain says that the learning rate needs to decay, but I don't see that # described in Randlov's paper. # A higher number here means the learning rate decays slower. learner.learningRateDecay = 2000 # NOTE increasing this number above from the default of 100 is what got the # learning to actually happen, and fixed the bug/issue where the performance # agent's performance stopped improving.
