/// <summary>
/// Choose an action.
/// </summary>
///
/// <param name="actionEstimates">Action estimates.</param>
///
/// <returns>Returns selected action.</returns>
///
/// <remarks>The method chooses an action depending on the provided estimates. The
/// estimates can be any sort of estimate, which values usefulness of the action
/// (expected summary reward, discounted reward, etc). The action is choosed from
/// non-tabu actions only.</remarks>
///
public int ChooseAction(double[] actionEstimates)
{
// get amount of non-tabu actions
int nonTabuActions = actions;
for (int i = 0; i < actions; i++)
{
if (tabuActions[i] != 0)
{
nonTabuActions--;
}
}
// allowed actions
double[] allowedActionEstimates = new double[nonTabuActions];
int[] allowedActionMap = new int[nonTabuActions];
for (int i = 0, j = 0; i < actions; i++)
{
if (tabuActions[i] == 0)
{
// allowed action
allowedActionEstimates[j] = actionEstimates[i];
allowedActionMap[j] = i;
j++;
}
else
{
// decrease tabu time of tabu action
tabuActions[i]--;
}
}
return(allowedActionMap[basePolicy.ChooseAction(allowedActionEstimates)]);;
}
/// <summary>
/// 从指定状态获取下一个动作。
/// Get next action from the specified state.
/// </summary>
///
/// <param name="state">要获取操作的当前状态。
/// Current state to get an action for.</param>
///
/// <returns>
/// 返回状态的动作
/// Returns the action for the state.</returns>
///
/// <remarks>
/// 该方法根据当前返回一个动作
/// The method returns an action according to current
/// <see cref="ExplorationPolicy">exploration policy</see>.</remarks>
///
public int GetAction(int state)
{
double[] qs = new double[actions];
for (int i = 0; i < actions; i++)
{
qs[i] = (qvalues[state][i] + qvalues2[state][i]) / 2;
}
return(explorationPolicy.ChooseAction(qs));
}
/// <summary>
/// 从指定状态获取下一个动作。
/// Get next action from the specified state.
/// </summary>
///
/// <param name="state">要获取操作的当前状态。
/// Current state to get an action for.</param>
///
/// <returns>
/// 返回状态的动作
/// Returns the action for the state.</returns>
///
/// <remarks>
/// 该方法根据当前返回一个动作
/// The method returns an action according to current
/// <see cref="ExplorationPolicy">exploration policy</see>.</remarks>
///
public int GetAction(int state)
{
double[] nextActionEstimations = qvalues[state];
double maxNextExpectedReward = nextActionEstimations[0];
for (int i = 1; i < actions; i++)
{
if (nextActionEstimations[i] > maxNextExpectedReward)
{
maxNextExpectedReward = nextActionEstimations[i];
}
}
return(explorationPolicy.ChooseAction(qvalues[state]));
}
/// <summary>
/// Get next action from the specified state.
/// </summary>
///
/// <param name="state">Current state to get an action for.</param>
///
/// <returns>Returns the action for the state.</returns>
///
/// <remarks>The method returns an action according to current
/// <see cref="ExplorationPolicy">exploration policy</see>.</remarks>
///
public int GetAction(int state)
{
return(explorationPolicy.ChooseAction(qvalues[state]));
}
/// <summary>
/// Get next action from the specified state.
/// </summary>
///
/// <param name="state">Current state to get an action for.</param>
///
/// <returns>Returns the action for the state.</returns>
///
/// <remarks>The method returns an action according to current
/// <see cref="ExplorationPolicy">exploration policy</see>.</remarks>
///
public int GetAction(int state)
{
return(explorationPolicy.ChooseAction(Q(state)));
}
public int GetAction(int state)
{
return(_explorationPolicy.ChooseAction(_rewardTable[state]));
}