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See:
Description
Interface Summary | |
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ActionSelector | An action selection policy for a reinforcement-learning algorithm. |
ContinuousLearner | A learning algorithm that outputs a continuous signal. |
DiscreteLearner | A learner that learns a discrete number of different actions. |
Learner | Classes implementing this interface indicate that they implement a learning algorithm. |
LearnerMonitor | |
MDPLearner | Classes implementing this interface implement learning algorithms for Markoff descision processes (MDPs). |
MimicryLearner | A learner that attempts to adjust its output to match a training signal. |
SelfKnowledgable | Classes implementing this interface indicate that they know if their output is good enough to be used. |
StimuliResponseLearner | Classes implementing this interface implement myopic stimuli-response reinformcement learning algorithms. |
Class Summary | |
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AbstractLearner | |
DumbLearner | A learner that chooses the same specified action on every iteration. |
DumbRandomLearner | A learner that simply plays a random action on each iteration without any learning. |
EpsilonGreedyActionSelector | An implementation of the epsilon-greedy action selection policy. |
GraphLearnerMonitor | |
MetaLearner | |
NPTRothErevLearner | A modification of RothErev to address parameter degeneracy, and modified learning with 0-reward. |
QLearner | An implementation of the Q-learning algorithm. |
RothErevLearner | A class implementing the Roth-Erev learning algorithm. |
SlidingWindowLearner | maintains a sliding window over the trained data series and use the average of data items falling into the window as the output learned. |
SoftMaxActionSelector | An implementation of the softmax action selection policy. |
StatelessQLearner | A memory-less version of the Q-Learning algorithm. |
WidrowHoffLearner | An implementation of the Widrow-Hoff learning algorithm for 1-dimensional training sets. |
WidrowHoffLearnerWithMomentum |
A library of algorithms for individual learning.
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