private void ComputeValueFunction(int iteration, IDeterministicPolicy <TState> policy, IEnumerable <TState> states)
{
if (!ValueFunctions[policy].TryGetValue(iteration - 1, out var previousFunction))
{
throw new InvalidOperationException("Previous function not available");
}
if (!ValueFunctions[policy].ContainsKey(iteration))
{
ValueFunctions[policy][iteration] = NextFunction();
}
var function = ValueFunctions[policy][iteration];
foreach (var state in states)
{
var action = policy[state];
var expectation = new Value(0.0, 0.0);
if (action != null)
{
expectation = GetExpectation(action, state, previousFunction);
}
function.Add(state, expectation);
}
}
private Value GetOptimalValue(IDeterministicPolicy <TState> policy, double tolerance = 1e-4, bool ignoreZeroChange = false, TState[] outputStates = null)
{
if (outputStates == null)
{
outputStates = new[] { _initialState }
}
;
Initialize(policy, outputStates);
var values = new Value[outputStates.Length];
var diff = new Value[outputStates.Length];
var change = double.MaxValue;
while ((change > tolerance || (change == 0 && !ignoreZeroChange) || values[0].Mean == 0) && _iteration < _maxIterations && StateSpace[_iteration].Count > 0)
{
var nextValues = Iterate(policy);
for (var i = 0; i < outputStates.Length; ++i)
{
diff[i] = nextValues[i] + ((-1) * values[i]);
}
change = Norm(diff);
if (LogIterations)
{
Log?.Info($"Iteration {_iteration} : {values[0].Mean} -> {nextValues[0].Mean} \t {change}");
}
values = nextValues;
}
return(values[0]);
}
private IDeterministicPolicy <TState> PolicyIteration(IDeterministicPolicy <TState> policy)
{
if (!UseReachableStateSpace)
{
return(PartialPolicyIteration(policy));
}
if (ReachableStates == null)
{
ReachableStates = GetReachableStateSpace(policy, _initialState);
}
var nextPolicy = (IDeterministicPolicy <TState>)policy.Clone();
var progress = 0.0;
var step = 1.0 / ReachableStates.Count;
var logStep = 0.01;
Log?.Info("Calculating optimal actions...");
foreach (var state in ReachableStates.Keys)
{
var optimalAction = GetOptimalActionEx(policy, state);
if (optimalAction != null)
{
nextPolicy[state] = optimalAction;
}
if ((progress / logStep) - Math.Truncate(progress / logStep) < step / logStep)
{
Log?.Info($"{Math.Truncate(progress * 10000) / 100}%");
}
progress += step;
}
return(nextPolicy);
}
private static void PrintPolicyActions(
GamblersWorld world,
IDeterministicPolicy <GamblersWorldState, GamblersWorldAction> policy)
{
foreach (var state in world.AllStates())
{
Console.WriteLine($"{state}: {policy.Action(state)}");
}
}
/// <summary>
/// Policy iteration for MDP
/// </summary>
/// <param name="basePolicy">Starting policy</param>
/// <param name="optimalPolicy">Resulting optimal policy</param>
/// <param name="tolerance">Convergence tolerance</param>
/// <returns>Optimal value</returns>
public double GetOptimalValueViaPolicyIteration(
IDeterministicPolicy <TState> basePolicy,
out IDeterministicPolicy <TState> optimalPolicy,
double tolerance = 0.0001)
{
var policy = (IDeterministicPolicy <TState>)basePolicy.Clone();
var node = Policies.AddFirst(basePolicy);
var value = GetOptimalValue(policy, tolerance);
optimalPolicy = policy = PolicyIteration(policy);
var outputStates = new List <TState> {
_initialState
};
outputStates.AddRange(policy.GetAllowedActions(_initialState).Where(a => a != null)
.SelectMany(a => a[_initialState]));
while (policy.IsModified)
{
node = Policies.AddAfter(node, policy);
var nextValue = GetOptimalValue(policy, tolerance, false, outputStates.Distinct().ToArray());
if (LogProgress)
{
Log?.Info($"{value.Mean}->{nextValue.Mean} at variance {Math.Sqrt(value.Variance - (value.Mean * value.Mean))}->{Math.Sqrt(nextValue.Variance - (nextValue.Mean * nextValue.Mean))} with {policy.Modifications.Length} modifications");
}
var ratio = value.Mean > 0 ? nextValue.Mean / value.Mean : value.Mean / nextValue.Mean;
if ((nextValue.Mean - (tolerance * 100) < value.Mean) || Math.Abs(ratio) < 1 + RelativeOptimalTolerance)
{
value = nextValue;
break;
}
value = nextValue;
policy = PolicyIteration(policy);
if (node.Previous == null)
{
continue;
}
ValueFunctions[node.Previous.Value].Clear();
ValueFunctions.Remove(node.Previous.Value);
Policies.Remove(node.Previous);
}
value = GetOptimalValue(policy, tolerance);
optimalPolicy = (IDeterministicPolicy <TState>)basePolicy.Clone();
foreach (var state in AllStateSpace)
{
optimalPolicy[state] = policy[state];
}
ValueFunctions.Clear();
Policies.Clear();
return(value.Mean);
}
public EnhancementGraph(IDeterministicPolicy <EnhancementState> policy, EnhancementState initialState, int iterations)
{
var nextStates = new HashSet <EnhancementState> {
initialState
};
var allStates = new HashSet <EnhancementState>();
while (iterations-- > 0)
{
var nextNextStates = new HashSet <EnhancementState>();
foreach (var state in nextStates)
{
var from = _graph.Vertices.SingleOrDefault(v => v.Label == state.ToString());
if (from == null)
{
from = new StateVertex(state);
_graph.AddVertex(from);
}
var action = policy[state];
if (action == null)
{
continue;
}
var actionStates = action[state].ToArray();
foreach (var s in actionStates)
{
var to = _graph.Vertices.SingleOrDefault(v => v.Label == s.ToString());
if (to == null)
{
to = new StateVertex(s);
_graph.AddVertex(to);
}
_graph.AddEdge(new ActionEdge(action)
{
Source = from,
Target = to,
});
}
nextNextStates.UnionWith(actionStates);
}
nextStates.Clear();
foreach (var s in nextNextStates)
{
if (allStates.Add(s))
{
nextStates.Add(s);
}
}
}
}
private IMarkovAction <TState> GetOptimalActionEx(IDeterministicPolicy <TState> policy, TState state)
{
var iterations = ValueFunctions[policy].Count - 1;
var lastValueFunction = ValueFunctions[policy][iterations];
var argmax = policy[state];
var value = new Value(double.MinValue, 0.0);
var baseValue = value;
if (lastValueFunction.HasState(state))
{
baseValue = value = lastValueFunction[state];
}
var deps = GetGreedyDependencies(policy, state);
for (var i = 1; i <= iterations; ++i)
{
var states = deps[iterations - i];
if (states.Count == 0)
{
continue;
}
ComputeValueFunction(i, policy, states);
}
lastValueFunction = ValueFunctions[policy][iterations];
foreach (var action in policy.GetAllowedActions(state))
{
if (action == null)
{
continue;
}
var expectation = GetExpectation(action, state, lastValueFunction);
if (expectation.Mean <= value.Mean)
{
continue;
}
value = new Value(expectation.Mean, expectation.Variance);
argmax = action;
}
if (baseValue.Mean != double.MinValue && value.Mean != baseValue.Mean)
{
Changes[state] = value.Mean - baseValue.Mean;
}
return(argmax);
}
private List <HashSet <TState> > GetGreedyDependencies(IDeterministicPolicy <TState> policy, TState state)
{
var list = new List <HashSet <TState> >();
var iterations = ValueFunctions[policy].Count - 1;
var valueFunction = ValueFunctions[policy][iterations];
var allowedActions = policy.GetAllowedActions(state).Where(a => a != null);
var prevPolicy = Policies.Find(policy)?.Previous?.Value;
var nextStates = new HashSet <TState>();
foreach (var action in allowedActions)
{
var actionStates = action[state].Where(s => !valueFunction.HasState(s));
nextStates.UnionWith(actionStates);
}
list.Add(nextStates);
while (iterations-- > 0)
{
var set = new HashSet <TState>();
valueFunction = ValueFunctions[policy][iterations];
foreach (var nextState in nextStates)
{
if (!UseReachableStateSpace && !policy.HasOptimal(nextState) && prevPolicy != null)
{
policy[nextState] = GetOptimalActionEx(prevPolicy, nextState);
}
var action = policy[nextState];
if (action == null)
{
continue;
}
// IEnumerable<TState> actionStates;
// lock (action)
var actionStates = action[nextState].Where(s => !valueFunction.HasState(s));
set.UnionWith(actionStates);
}
list.Add(set);
nextStates = set;
}
return(list);
}
private List <HashSet <TState> > GetValueDependencies(IDeterministicPolicy <TState> policy, TState state, int depth, IMarkovAction <TState> nextAction = null)
{
var deps = new List <HashSet <TState> >(depth + 1);
var it = 0;
var states = new HashSet <TState>();
var valueFunction = ValueFunctions[policy][depth];
// replace the policy action with next action
if (nextAction != null)
{
var actionStates = new List <TState>();
lock (nextAction)
actionStates.AddRange(nextAction[state].Where(s => !valueFunction.HasState(s)));
states.UnionWith(actionStates);
depth--;
}
else
{
states.Add(state);
}
deps.Add(states);
while (depth-- > 0)
{
++it;
var nextStates = new HashSet <TState>();
foreach (var s in states)
{
var action = policy[s];
if (action == null)
{
continue;
}
IEnumerable <TState> actionStates;
lock (action)
actionStates = action[s];
nextStates.UnionWith(actionStates);
}
deps.Add(nextStates);
states = nextStates;
}
return(deps);
}
private IMarkovAction <TState> GetOptimalAction(IDeterministicPolicy <TState> policy, TState state)
{
var iterations = ValueFunctions[policy].Count - 1;
var lastValueFunction = ValueFunctions[policy][iterations];
var argmax = policy[state];
var value = new Value(double.MinValue, 0.0);
if (lastValueFunction.HasState(state))
{
value = lastValueFunction[state];
}
var dict = GetAllowedActionsDependencies(policy, state);
foreach (var action in policy.GetAllowedActions(state))
{
if (action == null)
{
continue;
}
var deps = dict[action];
for (var i = 1; i <= iterations; ++i)
{
var valueFunction = ValueFunctions[policy][i];
var states = deps[iterations - i];
if (states.Count == 0)
{
continue;
}
var newStates = states.Where(s => !valueFunction.HasState(s));
ComputeValueFunction(i, policy, newStates);
}
lastValueFunction = ValueFunctions[policy][iterations];
var expectation = GetExpectation(action, state, lastValueFunction);
if (expectation.Mean <= value.Mean)
{
continue;
}
value = new Value(expectation.Mean, expectation.Variance);
argmax = action;
}
return(argmax);
}
/// <summary>
/// Populate states reachable from initial state within the current number of iterations
/// </summary>
private void ExtendStateSpace(IDeterministicPolicy <TState> policy)
{
var previousLayer = StateSpace[_iteration - 1];
if (StateSpace.ContainsKey(_iteration))
{
return;
}
var layer = new HashSet <TState>();
if (_iteration > 2)
{
layer = StateSpace[_iteration - 2];
layer.Clear();
}
var distinctLayer = new List <TState>();
var nextStates = new HashSet <TState>();
foreach (var state in previousLayer)
{
var action = policy[state];
if (action == null)
{
continue;
}
nextStates.UnionWith(action[state]);
}
foreach (var nextState in nextStates)
{
if (AllStateSpace.Add(nextState))
{
distinctLayer.Add(nextState);
}
layer.Add(nextState);
}
StateSpace[_iteration] = layer;
DistinctStateSpace[_iteration] = distinctLayer;
}
private IDeterministicPolicy <TState> PartialPolicyIteration(IDeterministicPolicy <TState> policy)
{
var nextPolicy = (IDeterministicPolicy <TState>)policy.Clone();
var dict = new Dictionary <TState, double> {
[_initialState] = 1.0
};
var allStates = new HashSet <TState> {
_initialState
};
// {0} -> A{0}
var nextStates = new HashSet <TState>();
nextPolicy[_initialState] = GetOptimalActionEx(policy, _initialState);
foreach (var action in policy.GetAllowedActions(_initialState).Where(a => a != null))
{
var states = action[_initialState].ToImmutableHashSet();
foreach (var state in states)
{
dict[state] = action[_initialState, state];
var optimalAction = GetOptimalActionEx(policy, state);
if (optimalAction == null)
{
continue;
}
nextPolicy[state] = optimalAction;
}
nextStates.UnionWith(states);
allStates.UnionWith(states);
}
var tol = MinProbability;
var maxProba = 1.0;
var logStep = 0.3;
var scale = -1 / Math.Log(tol, 10);
if (LogProgress)
{
Log?.Info("Calculating optimal actions...");
}
var prevMaxProba = 1.0;
while (maxProba > tol)
{
maxProba = 0.0;
var nextNextStates = new HashSet <TState>();
foreach (var state in nextStates)
{
var optimalAction = GetOptimalActionEx(policy, state);
if (optimalAction != null)
{
nextPolicy[state] = optimalAction;
}
var stateProba = dict[state];
if (stateProba > maxProba)
{
maxProba = stateProba;
}
var action = nextPolicy[state];
if (action == null)
{
continue;
}
var actionStates = action[state].ToImmutableHashSet();
foreach (var nextState in actionStates)
{
var p = action[state, nextState];
if (dict.TryGetValue(nextState, out var current))
{
dict[nextState] = Math.Max(current, stateProba * p);
}
else
{
dict.Add(nextState, stateProba * p);
}
}
nextNextStates.UnionWith(actionStates);
}
if (Math.Log(prevMaxProba, 10) - Math.Log(maxProba, 10) > logStep && LogProgress)
{
Log?.Info(
$"{Math.Truncate(Math.Min(-Math.Log(maxProba, 10) * scale, 1) * 10000) / 100}%");
prevMaxProba = maxProba;
}
nextStates.Clear();
foreach (var state in nextNextStates)
{
if (allStates.Add(state))
{
nextStates.Add(state);
}
}
}
return(nextPolicy);
}
private Dictionary <IMarkovAction <TState>, List <HashSet <TState> > > GetAllowedActionsDependencies(IDeterministicPolicy <TState> policy, TState state)
{
var iterations = ValueFunctions[policy].Count - 1;
var dict = new Dictionary <IMarkovAction <TState>, List <HashSet <TState> > >();
Parallel.ForEach(policy.GetAllowedActions(state).Where(a => a != null), new ParallelOptions {
MaxDegreeOfParallelism = Configuration.ThreadsPerInstance
}, action =>
{
var deps = GetValueDependencies(policy, state, iterations, action);
lock (dict)
dict[action] = deps;
});
return(dict);
}
private Dictionary <TState, double> GetReachableStateSpace(IDeterministicPolicy <TState> policy, TState initialState)
{
var dict = new Dictionary <TState, double>();
var tol = MinProbability;
var maxProba = 1.0;
var allStates = new HashSet <TState>();
var states = new HashSet <TState> {
initialState
};
dict[initialState] = 1.0;
var it = 0;
while (maxProba > tol && states.Count > 0)
{
maxProba = 0.0;
var allowedStates = new HashSet <TState>();
foreach (var state in states)
{
var allowedActions = policy.GetAllowedActions(state).Where(a => a != null).ToArray();
var stateProba = dict[state];
if (stateProba > maxProba)
{
maxProba = stateProba;
}
if (stateProba < tol)
{
continue;
}
foreach (var action in allowedActions)
{
var nextStates = action[state].ToImmutableHashSet();
foreach (var nextState in nextStates)
{
var p = action[state, nextState];
if (dict.TryGetValue(nextState, out var current))
{
dict[nextState] = Math.Max(current, stateProba * p);
}
else
{
dict.Add(nextState, stateProba * p);
}
}
allowedStates.UnionWith(nextStates);
}
}
states.Clear();
foreach (var allowedState in allowedStates)
{
if (allStates.Add(allowedState))
{
states.Add(allowedState);
}
}
++it;
}
return(dict);
}
public bool HasSameActionsAs(IDeterministicPolicy <TState, TAction> otherPolicy)
{
return(_actions.Keys.All(state =>
_actions[state].Equals(otherPolicy.Action(state))));
}
