Added initial files

1 files changed, 102 insertions, 0 deletions

diff --git a/QTable/qtsnake.py b/QTable/qtsnake.py
new file mode 100755
index 0000000..4b1eac7
--- /dev/null
+++ b/QTable/qtsnake.py
@@ -0,0 +1,102 @@
+#+AUTHOR: bdunahu
+#+TITLE: qtsnake.py
+#+DESCRIPTION qtable lookup, training, and handling for multiagent snake
+
+import numpy as np
+from GameEngine import multiplayer
+from collections import namedtuple
+
+WINDOW_WIDTH = None
+WINDOW_HEIGHT = None
+GAME_UNITS = None
+
+Point = namedtuple('Point', 'x, y')
+
+
+def sense_goal(head, goal):
+    '''
+    maps head and goal location onto an
+    integer corresponding to approx location
+    '''
+    diffs = Point(goal.x - head.x, goal.y - head.y)
+
+    if diffs.x == 0 and diffs.y <  0:
+        return 0
+    if diffs.x >  0 and diffs.y <  0:
+        return 1
+    if diffs.x >  0 and diffs.y == 0:
+        return 2
+    if diffs.x >  0 and diffs.y >  0:
+        return 3
+    if diffs.x == 0 and diffs.y >  0:
+        return 4
+    if diffs.x <  0 and diffs.y >  0:
+        return 5
+    if diffs.x <  0 and diffs.y == 0:
+        return 6
+    return 7
+
+def load_q(filename):
+    ''' loads np array from given file '''
+    if not filename.endswith('.npy'):
+        exit(1)
+    return np.load(filename)
+
+class QSnake:
+    def __init__(self, game_engine):
+        ''' initialize fields required by model '''
+        self.game_engine = game_engine
+
+    def index_actions(self, q, pid):
+        '''
+        given q, player_id, an array of heads,
+        and the goal position,
+        indexes into the corresponding expected
+        reward of each action
+        '''
+        heads, tails, goal = self.game_engine.get_heads_tails_and_goal()
+        state = sense_goal(heads[pid], goal)
+        return state, q[state, :]
+
+    def argmin_gen(self, rewards):
+        '''
+        Given an array of rewards indexed by actions,
+        yields actions in order from most rewarding to
+        least rewarding
+        '''
+        rewards = rewards.copy()
+        for i in range(rewards.size):
+            best_action = np.argmin(rewards)
+            rewards[best_action] = float("inf")
+            yield best_action
+
+    def pick_greedy_action(self, q, pid, epsilon):
+        '''
+        given a q table, the id of the player
+        taking action, and a randomization factor,
+        returns the most rewarding non-lethal action
+        or a non-lethal random action.
+        '''
+        viable_actions = self.game_engine.get_viable_actions(pid)
+        state, rewards = self.index_actions(q, pid)
+
+        if np.random.uniform() < epsilon:
+            return (state, np.random.choice(viable_actions)) if viable_actions.size > 0 else (state, 0)
+        for action in self.argmin_gen(rewards):
+            if action in viable_actions:
+                return (state, action)
+        return (state, 0) # death
+
+    def update_q(self, q, old_state_action, new_state_action, outcome, lr=0.05):
+        '''
+        given a q table, the previous state/action pair,
+        the new state/action pair, the outcome of the last
+        action, and the learning rate
+        updates q with the temporal difference.
+        '''
+        if outcome == multiplayer.CollisionType.GOAL:
+            q[new_state_action[0], new_state_action[1]] = 0
+        else:
+            td_error = -1 + q[new_state_action[0], new_state_action[1]] - q[old_state_action[0], old_state_action[1]]
+            q[old_state_action[0], old_state_action[1]] += lr * td_error
+