BatchLinUCBDisjointPolicyEpsilon: Batch Disjoint LinUCB Policy with Epsilon-Greedy
In cramR: Cram Method for Efficient Simultaneous Learning and Evaluation
BatchLinUCBDisjointPolicyEpsilon R Documentation
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Description
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Details
Implements the disjoint LinUCB algorithm with upper confidence bounds and epsilon-greedy exploration, using batched updates.
Methods
- 'initialize(alpha = 1.0, epsilon = 0.1, batch_size = 1)': Constructor.
- 'set_parameters(context_params)': Initializes sufficient statistics for each arm.
- 'get_action(t, context)': Selects an arm using UCB scores and epsilon-greedy rule.
- 'set_reward(t, context, action, reward)': Updates statistics and refreshes model at batch intervals.
Super class
cramR::NA -> BatchLinUCBDisjointPolicyEpsilon
Public fields
alphaNumeric, UCB exploration strength parameter.
epsilonNumeric, probability of taking a random exploratory action.
batch_sizeInteger, number of rounds per batch update.
A_ccList of Gram matrices per arm, accumulated across batch.
b_ccList of reward-weighted context vectors per arm.
class_nameInternal class name identifier.
Methods
Public methods
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Inherited methods
Method new()
Constructor for batched LinUCB with epsilon-greedy exploration.
Usage
BatchLinUCBDisjointPolicyEpsilon$new(alpha = 1, epsilon = 0.1, batch_size = 1)
Arguments
alphaNumeric. UCB width parameter (exploration strength).
epsilonNumeric. Probability of selecting a random arm.
batch_sizeInteger. Number of rounds before updating parameters.
Method set_parameters()
Initialize arm-specific parameter containers.
Usage
BatchLinUCBDisjointPolicyEpsilon$set_parameters(context_params)
Arguments
context_paramsList containing at least 'unique' (feature size) and 'k' (number of arms).
Method get_action()
Chooses an arm based on UCB and epsilon-greedy sampling.
Usage
BatchLinUCBDisjointPolicyEpsilon$get_action(t, context)
Arguments
tInteger timestep.
contextList containing the context for the decision.
Returns
A list with the selected action.
Method set_reward()
Updates arm-specific sufficient statistics based on observed reward.
Parameter updates occur only at the end of a batch.
Usage
BatchLinUCBDisjointPolicyEpsilon$set_reward(t, context, action, reward)
Arguments
tInteger timestep.
contextContext object used for decision-making.
actionList containing the chosen action.
rewardList containing the observed reward.
Returns
Updated internal model parameters.
Method clone()
The objects of this class are cloneable with this method.
Usage
BatchLinUCBDisjointPolicyEpsilon$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
cramR documentation built on Aug. 25, 2025, 1:12 a.m.
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| BatchLinUCBDisjointPolicyEpsilon | R Documentation |
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Description
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Batch Disjoint LinUCB Policy with Epsilon-Greedy
Details
Implements the disjoint LinUCB algorithm with upper confidence bounds and epsilon-greedy exploration, using batched updates.
Methods
- 'initialize(alpha = 1.0, epsilon = 0.1, batch_size = 1)': Constructor. - 'set_parameters(context_params)': Initializes sufficient statistics for each arm. - 'get_action(t, context)': Selects an arm using UCB scores and epsilon-greedy rule. - 'set_reward(t, context, action, reward)': Updates statistics and refreshes model at batch intervals.
Super class
cramR::NA -> BatchLinUCBDisjointPolicyEpsilon
Public fields
alphaNumeric, UCB exploration strength parameter.
epsilonNumeric, probability of taking a random exploratory action.
batch_sizeInteger, number of rounds per batch update.
A_ccList of Gram matrices per arm, accumulated across batch.
b_ccList of reward-weighted context vectors per arm.
class_nameInternal class name identifier.
Methods
Public methods
Inherited methods
Method new()
Constructor for batched LinUCB with epsilon-greedy exploration.
Usage
BatchLinUCBDisjointPolicyEpsilon$new(alpha = 1, epsilon = 0.1, batch_size = 1)
Arguments
alphaNumeric. UCB width parameter (exploration strength).
epsilonNumeric. Probability of selecting a random arm.
batch_sizeInteger. Number of rounds before updating parameters.
Method set_parameters()
Initialize arm-specific parameter containers.
Usage
BatchLinUCBDisjointPolicyEpsilon$set_parameters(context_params)
Arguments
context_paramsList containing at least 'unique' (feature size) and 'k' (number of arms).
Method get_action()
Chooses an arm based on UCB and epsilon-greedy sampling.
Usage
BatchLinUCBDisjointPolicyEpsilon$get_action(t, context)
Arguments
tInteger timestep.
contextList containing the context for the decision.
Returns
A list with the selected action.
Method set_reward()
Updates arm-specific sufficient statistics based on observed reward. Parameter updates occur only at the end of a batch.
Usage
BatchLinUCBDisjointPolicyEpsilon$set_reward(t, context, action, reward)
Arguments
tInteger timestep.
contextContext object used for decision-making.
actionList containing the chosen action.
rewardList containing the observed reward.
Returns
Updated internal model parameters.
Method clone()
The objects of this class are cloneable with this method.
Usage
BatchLinUCBDisjointPolicyEpsilon$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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