See: Description
Interface | Description |
---|---|
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 | Description |
---|---|
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 |
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