Updates

docs/API/Models/OffPolicyMonteCarloControl.md

@@ -0,0 +1,61 @@
+# [API Reference](../../API.md) - [Models](../Models.md) - OffPolicyMonteCarloControl
+
+OffPolicyMonteCarloControl is a neural network with reinforcement learning capabilities. It can predict any positive numbers of discrete values.
+
+## Constructors
+
+### new()
+
+Create new model object. If any of the arguments are nil, default argument values for that argument will be used.
+
+```
+OffPolicyMonteCarloControl.new({targetPolicyFunction: string, discountFactor: number}): ModelObject
+```
+
+#### Parameters:
+
+* targetPolicyFunction: A function that defines the target policy used to select actions. The policy should be based on the current Q-values (or state-action values). This function determines how the agent chooses actions based on its current knowledge. Available options include:
+
+	* Greedy: Selects the action with the highest Q-value for a given state. This is typically the optimal policy, assuming the Q-values are accurate.
+
+	* Softmax: Selects actions probabilistically, where actions with higher Q-values are more likely to be chosen. The probability of selecting an action is determined by a temperature parameter that controls the exploration-exploitation trade-off.
+
+	* StableSoftmax: The more stable option of Softmax (Default)
+
+* discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.
+
+#### Returns:
+
+* ModelObject: The generated model object.
+
+## Functions
+
+### setParameters()
+
+Set model's parameters. When any of the arguments are nil, previous argument values for that argument will be used.
+
+```
+OffPolicyMonteCarloControl:setParameters({targetPolicyFunction: string, discountFactor: number})
+```
+
+#### Parameters:
+
+* targetPolicyFunction: A function that defines the target policy used to select actions. The policy should be based on the current Q-values (or state-action values). This function determines how the agent chooses actions based on its current knowledge. Available options include:
+
+	* Greedy: Selects the action with the highest Q-value for a given state. This is typically the optimal policy, assuming the Q-values are accurate.
+
+	* Softmax: Selects actions probabilistically, where actions with higher Q-values are more likely to be chosen. The probability of selecting an action is determined by a temperature parameter that controls the exploration-exploitation trade-off.
+
+	* StableSoftmax: The more stable option of Softmax (Default)
+
+* discountFactor: The higher the value, the more likely it focuses on long-term outcomes. The value must be set between 0 and 1.
+
+## Inherited From
+
+* [ReinforcementLearningBaseModel](ReinforcementLearningBaseModel.md)
+
+## References
+
+* [Off-Policy Monte Carlo Control, Page 90](http://incompleteideas.net/book/bookdraft2017nov5.pdf)
+
+* [Forgetting Early Estimates in Monte Carlo Control Methods](https://ev.fe.uni-lj.si/3-2015/Vodopivec.pdf)

docs/VersionHistory/BetaVersionHistory.md

@@ -1,8 +1,24 @@
 # Beta Version
 
+## Version 1.7
+
+[1.7.0](Beta/1-7-0.md) -- 8/1/2025
+
+## Version 1.6
+
+[1.6.0](Beta/1-6-0.md)
+
+## Version 1.5
+
+[1.5.0](Beta/1-5-0.md)
+
+## Version 1.4
+
+[1.4.0](Beta/1-4-0.md)
+
 ## Version 1.3
 
-[1.3.0](Beta/1-3-0.md) -- 24/12/2024
+[1.3.0](Beta/1-3-0.md) 
 
 ## Version 1.2
 

docs/VersionHistory/ReleaseVersionHistory.md

@@ -2,6 +2,7 @@
 
 | Version               | Number Of Blocks | Number Of Models | Number Of Optimizers | Number Of Cost Functions | Number Of Containers | Number of Utilities | Number Of Regularizers | Backward Incompatible Changes |
 |-----------------------|------------------|------------------|----------------------|--------------------------|----------------------|---------------------|------------------------|-------------------------------|
+| [1.8](Release/1-8.md) | 70               | 22               | 8                    | 5                        | 2                    | 3                   | 3                      | No                            |
 | [1.7](Release/1-7.md) | 70               | 20               | 8                    | 5                        | 2                    | 3                   | 3                      | No                            |
 | [1.6](Release/1-6.md) | 65               | 20               | 8                    | 5                        | 2                    | 3                   | 3                      | No                            |
 | [1.5](Release/1-5.md) | 61               | 20               | 8                    | 5                        | 2                    | 3                   | 3                      | No                            |