diff --git a/test/test-rl2.js b/test/test-rl2.js
new file mode 100644
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--- /dev/null
+++ b/test/test-rl2.js
@@ -0,0 +1,131 @@
+// Maze of Torment World
+// Dynamic Programming (DP)
+
+var height=7,width=7,start=0;
+var UPDATES=15
+// 0: free place, 1: start, 2: destination, -1: wall
+var f=0,s=1,d=2,w=-1
+var maze = [
+[s,f,w,d,w,f,f], 
+[f,f,w,f,w,f,f], 
+[f,f,w,f,f,f,f], 
+[f,f,w,w,w,f,f], 
+[f,f,f,f,f,f,f], 
+[f,f,f,f,w,w,w], 
+[f,w,f,f,f,f,f], 
+]
+
+var states = []
+maze.forEach(function (row) {
+  states=states.concat(row)
+})
+var rewards = states.map(function (s) {
+  return s==w?-1:(s==d?1:0)
+})
+
+var actions = ['left','right','up','down']
+
+var env = {};
+
+env.steps = 0;
+env.iteration = 0;
+
+var way = []
+function reset (pr) {
+  if (pr) print(way.join('\n'))
+  way = maze.map(function (row) { 
+    return row.map(function (col) { return col==s?1:(col==w?'w':0) })})
+  env.steps=0;
+}
+
+// required by learner
+env.getNumStates      = function() { return height*width; }
+env.getMaxNumActions  = function() { return actions.length; }
+env.nextState = function(state,action,pr) {
+  var nx, ny, nextstate;
+  var x = env.stox(state);
+  var y = env.stoy(state);
+  switch (states[state]) {
+    case f: 
+    case s: 
+      // free place to move around
+      switch (action) {
+        case 'left'  : nx=x-1; ny=y; break;
+        case 'right' : nx=x+1; ny=y; break;
+        case 'up'    : ny=y-1; nx=x; break;
+        case 'down'  : ny=y+1; nx=x; break;
+      }
+      nextstate = ny*width+nx;
+      way[ny][nx]=1;
+      env.steps++;
+      break;
+    case w:
+      // cliff! oh no! Should not happend - see below
+      // print('Back to start...')
+      nextstate=start;
+      reset()
+      env.iteration++;
+      break;
+    case d:
+      // agent wins! teleport to start
+      if (pr) print('['+env.iteration+'] Found destination !!!!!!! steps='+env.steps)
+      reset(pr)
+      nextstate=start;
+      env.iteration++;
+      break;
+  }
+//print(state,action,nextstate)
+  return nextstate;
+}
+env.reward = function (state,action,nextstate) {
+  // reward of being in s, taking action a, and ending up in ns
+  var reward;
+  // If the destination was found, weight the reward with the number of steps
+  // return best reward for shortest path
+  if (states[state]==d) reward = rewards[state];
+  else reward = rewards[state];
+  return reward;
+}
+env.allowedActions    = function(state) { 
+  var x = env.stox(state), y = env.stoy(state);
+  var actions=[];
+  if (x>0) actions.push('left');
+  if (y>0) actions.push('up');
+  if (x<width-1) actions.push('right');
+  if (y<height-1) actions.push('down');
+  return actions 
+}
+
+// utils
+env.stox = function (s) { return s % width }
+env.stoy = function (s) { return Math.floor(s / width) }
+
+
+// create the DQN agent
+var model = ml.learn({
+  algorithm   : ml.ML.RL,
+  kind        : ml.ML.DPAgent,
+  actions     : actions,
+  
+  gamma       : 0.9,  // discount factor, [0, 1)
+  
+  environment : env
+}); 
+
+print(model)
+print(toJSON(model).length+' Bytes')
+
+reset()
+var state = start;  // uppel left corner
+for(var i=0;i<UPDATES;i++) ml.update(model)
+print('Required '+env.iteration+' iterations')
+reset()
+var timer = setInterval(function(){ // start the learning loop
+  var action = ml.action(model,state); // s is an integer
+  //... execute action in environment and get the reward
+  // print(state,action,states[state])
+  var ns = env.nextState(state,action,true);
+  //var reward = env.reward(ns)-0.01
+  //ml.update(model)
+  state = ns
+}, 100);