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computational-neuroscience

Reinforcement Learning Mouse Model of Maze Discovery

SYDE 552

Winter 2023

April 21st 2023

Importing neccesary libraries for data creation and visualization 

import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD , Adam, RMSprop
from keras.layers.activation.prelu import PReLU
from random import randint
import os, sys, time, datetime, json, random

Code for simulating a rat in a maze with actions, agent and reward 

# dcreate the colors
visited_mark = 0.8  # Cells visited by the rat will be painted by gray 0.8
mouse_mark = 0.5  # The current rat cell will be painteg by gray 0.5

# numerically assign valus to possible actions
# assume rat cannot move diagonal
LEFT = 0
UP = 1
RIGHT = 2
DOWN = 3

# Actions dictionary
actions_dict: dict[int, str] = {
    LEFT: "left",
    UP: "up",
    RIGHT: "right",
    DOWN: "down",
}

num_actions: int = len(actions_dict)

# Exploration factor
#  one of every 10 moves the agent takes a completely random action
epsilon: float = 1 / 10


# maze is a 2d Numpy array of floats between 0.0 to 1.0
# 1.0 corresponds to a free cell, and 0.0 an occupied cell
# mouse = (row, col) initial mouse position (defaults to (0,0))


class Qmaze(object):
    def __init__(
        self,
        maze: list,
        mouse: list = (0, 0),
        valid_penalty: float = -0.04,
        invalid_penality: float = -0.75,
        visited_penality: float = -0.25,
    ):
        self._maze = np.array(maze)
        nrows, ncols = self._maze.shape
        self._valid_penality = valid_penalty
        self._invalid_penality = invalid_penality
        self._visited_penality = visited_penality

        # target cell where the "cheese" is
        # the default behaviour is that the cheese is always in the
        # bottom right corner of the maze
        self.target = (nrows - 1, ncols - 1)

        # create free cells
        self.free_cells = [
            (r, c)
            for r in range(nrows)
            for c in range(ncols)
            if self._maze[r, c] == 1.0
        ]
        # remove the target from the "free cells"
        self.free_cells.remove(self.target)

        # throw an exception if there is no way to get to the target cell
        if self._maze[self.target] == 0.0:
            raise Exception("Invalid maze: target cell cannot be blocked!")

        # throw an exception if the mouse is not started on a free cell
        if not mouse in self.free_cells:
            raise Exception("Invalid mouse Location: must sit on a free cell")
        self.reset(mouse)

    def reset(self, mouse):
        self.mouse = mouse
        self.maze = np.copy(self._maze)
        nrows, ncols = self.maze.shape
        row, col = mouse
        self.maze[row, col] = mouse_mark
        self.state = (row, col, "start")
        self.min_reward = -0.5 * self.maze.size
        self.total_reward = 0
        self.visited = set()

    def update_state(self, action):
        nrows, ncols = self.maze.shape
        nrow, ncol, nmode = mouse_row, mouse_col, mode = self.state

        if self.maze[mouse_row, mouse_col] > 0.0:
            self.visited.add((mouse_row, mouse_col))  # mark visited cell

        valid_actions = self.valid_actions()

        if not valid_actions:
            nmode = "blocked"
        elif action in valid_actions:
            nmode = "valid"
            if action == LEFT:
                ncol -= 1
            elif action == UP:
                nrow -= 1
            if action == RIGHT:
                ncol += 1
            elif action == DOWN:
                nrow += 1
        else:  # invalid action, no change mouse position
            mode = "invalid"

        # new state
        self.state = (nrow, ncol, nmode)

    def get_reward(self):
        mouse_row, mouse_col, mode = self.state
        nrows, ncols = self.maze.shape
        valid_penalty = self._valid_penality
        invalid_penalty = self._invalid_penality
        visited_penalty = self._visited_penality
        if mouse_row == nrows - 1 and mouse_col == ncols - 1:
            return 1.0
        if mode == "blocked":
            return self.min_reward - 1
        if (mouse_row, mouse_col) in self.visited:
            return visited_penalty
        if mode == "invalid":
            return invalid_penalty
        if mode == "valid":
            return valid_penalty

    def act(self, action: int):
        self.update_state(action)
        reward = self.get_reward()
        self.total_reward += reward
        status = self.trial_status()
        env_state = self.observe()
        return env_state, reward, status

    def observe(self):
        canvas = self.create_environment()
        env_state = canvas.reshape((1, -1))
        return env_state

    def create_environment(self):
        canvas = np.copy(self.maze)
        nrows, ncols = self.maze.shape
        # clear all visual marks
        for r in range(nrows):
            for c in range(ncols):
                if canvas[r, c] > 0.0:
                    canvas[r, c] = 1.0
        # draw the mouse
        row, col, valid = self.state
        canvas[row, col] = mouse_mark
        return canvas

    def trial_status(self):
        if self.total_reward < self.min_reward:
            return "lose"
        mouse_row, mouse_col, mode = self.state
        nrows, ncols = self.maze.shape
        if mouse_row == nrows - 1 and mouse_col == ncols - 1:
            return "win"
        return "not_over"

    def valid_actions(self, cell=None):
        if cell is None:
            row, col, _ = self.state
        else:
            row, col = cell
        actions = [0, 1, 2, 3]
        nrows, ncols = self.maze.shape
        if row == 0:
            actions.remove(1)
        elif row == nrows - 1:
            actions.remove(3)

        if col == 0:
            actions.remove(0)
        elif col == ncols - 1:
            actions.remove(2)

        if row > 0 and self.maze[row - 1, col] == 0.0:
            actions.remove(1)
        if row < nrows - 1 and self.maze[row + 1, col] == 0.0:
            actions.remove(3)

        if col > 0 and self.maze[row, col - 1] == 0.0:
            actions.remove(0)
        if col < ncols - 1 and self.maze[row, col + 1] == 0.0:
            actions.remove(2)

        return actions


# show 8x8 maze | WALL = BLACK | MOUSE = DARK GRAY | PATH = LIGHT GRAY | CHEESE = VERY LIGHT GRAY
def show(qmaze: Qmaze):
    plt.grid("on")
    nrows, ncols = qmaze.maze.shape
    ax = plt.gca()
    ax.set_xticks(np.arange(0.5, nrows, 1))
    ax.set_yticks(np.arange(0.5, ncols, 1))
    ax.set_xticklabels([])
    ax.set_yticklabels([])
    canvas = np.copy(qmaze.maze)
    for row, col in qmaze.visited:
        canvas[row, col] = 0.6
    mouse_row, mouse_col, _ = qmaze.state
    canvas[mouse_row, mouse_col] = 0.3  # mouse cell
    canvas[nrows - 1, ncols - 1] = 0.9  # cheese cell
    img = plt.imshow(canvas, interpolation="none", cmap="gray")
    return img

Generate Random Maze

Applying Depth First Search (DFS) to generate random maze based on entrance and exit (aka cheese) location adapted from https://www.geeksforgeeks.org/random-acyclic-maze-generator-with-given-entry-and-exit-point/

# Class to define structure of a node
class Node:
    def __init__(self, value=None, next_element=None):
        self.val = value
        self.next = next_element


# Class to implement a stack
class stack:
    # Constructor
    def __init__(self):
        self.head = None
        self.length = 0

    # Put an item on the top of the stack
    def insert(self, data):
        self.head = Node(data, self.head)
        self.length += 1

    # Return the top position of the stack
    def pop(self):
        if self.length == 0:
            return None
        else:
            returned = self.head.val
            self.head = self.head.next
            self.length -= 1
            return returned

    # Return False if the stack is empty
    # and true otherwise
    def not_empty(self):
        return bool(self.length)

    # Return the top position of the stack
    def top(self):
        return self.head.val


def generate_random_maze(
    rows: int = 4,
    columns: int = 4,
    initial_point: list = (0, 0),
    final_point: list = (3, 3),
):
    ROWS, COLS = rows, columns

    # Array with only walls (where paths will
    # be created)
    maze = list(list(0 for _ in range(COLS)) for _ in range(ROWS))

    # Auxiliary matrices to avoid cycles
    seen = list(list(False for _ in range(COLS)) for _ in range(ROWS))
    previous = list(list((-1, -1) for _ in range(COLS)) for _ in range(ROWS))

    S = stack()

    # Insert initial position
    S.insert(initial_point)

    # Keep walking on the graph using dfs
    # until we have no more paths to traverse
    # (create)
    while S.not_empty():
        # Remove the position of the Stack
        # and mark it as seen
        x, y = S.pop()
        seen[x][y] = True

        # This is to avoid cycles with adj positions
        if (x + 1 < ROWS) and maze[x + 1][y] == 1 and previous[x][y] != (x + 1, y):
            continue
        if (0 < x) and maze[x - 1][y] == 1 and previous[x][y] != (x - 1, y):
            continue
        if (y + 1 < COLS) and maze[x][y + 1] == 1 and previous[x][y] != (x, y + 1):
            continue
        if (y > 0) and maze[x][y - 1] == 1 and previous[x][y] != (x, y - 1):
            continue

        # Mark as walkable position
        maze[x][y] = 1

        # Array to shuffle neighbours before
        # insertion
        to_stack = []

        # Before inserting any position,
        # check if it is in the boundaries of
        # the maze
        # and if it were seen (to avoid cycles)

        # If adj position is valid and was not seen yet
        if (x + 1 < ROWS) and seen[x + 1][y] == False:
            # Mark the adj position as seen
            seen[x + 1][y] = True

            # Memorize the position to insert the
            # position in the stack
            to_stack.append((x + 1, y))

            # Memorize the current position as its
            # previous position on the path
            previous[x + 1][y] = (x, y)

        if (0 < x) and seen[x - 1][y] == False:
            # Mark the adj position as seen
            seen[x - 1][y] = True

            # Memorize the position to insert the
            # position in the stack
            to_stack.append((x - 1, y))

            # Memorize the current position as its
            # previous position on the path
            previous[x - 1][y] = (x, y)

        if (y + 1 < COLS) and seen[x][y + 1] == False:
            # Mark the adj position as seen
            seen[x][y + 1] = True

            # Memorize the position to insert the
            # position in the stack
            to_stack.append((x, y + 1))

            # Memorize the current position as its
            # previous position on the path
            previous[x][y + 1] = (x, y)

        if (y > 0) and seen[x][y - 1] == False:
            # Mark the adj position as seen
            seen[x][y - 1] = True

            # Memorize the position to insert the
            # position in the stack
            to_stack.append((x, y - 1))

            # Memorize the current position as its
            # previous position on the path
            previous[x][y - 1] = (x, y)

        # Indicates if Pf is a neighbour position
        pf_flag = False
        while len(to_stack):
            # Remove random position
            neighbour = to_stack.pop(randint(0, len(to_stack) - 1))

            # Is the final position,
            # remember that by marking the flag
            if neighbour == final_point:
                pf_flag = True

            # Put on the top of the stack
            else:
                S.insert(neighbour)

        # This way, Pf will be on the top
        if pf_flag:
            S.insert(final_point)

    # Mark the initial position
    x0, y0 = initial_point
    xf, yf = final_point
    maze[x0][y0] = 1
    maze[xf][yf] = 1

    # Return maze formed by the traversed path
    return np.asarray(maze, dtype="float")


# Test Run to ensure that function is working correctly
test_cols = 8
test_rows = 8
test_init_point = (0, 0)
test_final_point = (7, 7)

test_maze = generate_random_maze(
    rows=test_rows,
    columns=test_cols,
    initial_point=test_init_point,
    final_point=test_final_point,
)
# check that the shape generated is correct
assert test_maze.shape == (test_cols, test_rows)

Generating a maze array and initializing a Qmaze 

maze = generate_random_maze(2, 2, (0, 0), (1, 1))
qmaze = Qmaze(maze=maze)
show(qmaze)

<matplotlib.image.AxesImage at 0x12e786790>

png

class Model(object):
    def __init__(self, maze: list, learning_rate: float = 0.001):
        model = Sequential()
        model.add(Dense(maze.size, input_shape=(maze.size,)))
        model.add(PReLU())
        model.add(Dense(maze.size))
        model.add(PReLU())
        model.add(Dense(num_actions))
        model.compile(optimizer="adam", loss="mse")
        self.model = model

    def get_model(self):
        return self.model

Create a Trial

Create an Trial class that accepts a trained neural network which calculates the next action, a Qmaze and the initial cell that the mouse is in.

class Trial:
    def __init__(self, model: Model, qmaze: Qmaze, mouse_cell: list):
        self._qmaze = qmaze
        self._model = model
        self.mouse_cell = mouse_cell

    def run(self):
        print("running trial...")
        mouse_cell = self._get_mouse_cell()
        self._qmaze.reset(mouse_cell)
        env_state = self._qmaze.observe()
        while True:
            prev_env_state = env_state
            Q = self._model.get_model().predict(prev_env_state)
            action = np.argmax(Q[0])
            _, _, status = self._qmaze.act(action)
            if status == "win":
                return True
            elif status == "lose":
                return False

    # For small mazes we can allow ourselves to perform a completion check in which we simulate all possible
    # games and check if our model wins the all. This is not practical for large mazes as it slows down training.
    def check(self):
        self._qmaze = self._get_maze()
        for cell in self._qmaze.free_cells:
            if not self._qmaze.valid_actions(cell):
                return False
            if not self.run():
                return False
        return True

    def _get_maze(self):
        return self._qmaze

    def _get_model(self):
        return self._model

    def _get_mouse_cell(self):
        return self.mouse_cell

Creating a Class to Model the Experience of the Mouse

Create an Experience class that collects the experience of Experiments within a list of memory. It retreives a model, a max_memory which defines the maximum amount of experiments that the mouse can remember and a discount factor which represents the instantanious uncertainty in the Bellman equation for stochastic environments.

class Experience(object):
    def __init__(self, model: Model, max_memory: int = 8, discount: float = 95 / 100):
        self.model = model
        self.max_memory = max_memory
        self.discount = discount
        self.memory = list()
        self.actions = model.get_model().output_shape[-1]

    def remember(self, trial):
        self.memory.append(trial)
        if len(self.memory) > self.max_memory:
            # delete the first element of the memory list if we exceed the max memory
            del self.memory[0]

    def predict(self, env_state):
        return self.model.get_model().predict(env_state)[0]

    def data(self, data_size: int = 10):
        environment_size = self.memory[0][0].shape[1]
        memory_size = len(self.memory)
        data_size = min(memory_size, data_size)
        inputs = np.zeros((data_size, environment_size))
        targets = np.zeros((data_size, self.actions))
        for idx, jdx in enumerate(
            np.random.choice(range(memory_size), data_size, replace=False)
        ):
            env_state, action, reward, env_state_next, trial_over = self.memory[jdx]
            inputs[idx] = env_state
            # There should be no target values for actions not taken.
            targets[idx] = self.predict(env_state)
            # Q_sa = derived policy = max quality env/action = max_a' Q(s', a')
            Q_sa = np.max(self.predict(env_state_next))
            if trial_over:
                targets[idx, action] = reward
            else:
                # reward + gamma * max_a' Q(s', a')
                targets[idx, action] = reward + self.discount * Q_sa
        return inputs, targets

Q-Training Algorithm for Reinforcement Learning of Mouse

The algorithm accepts the a number_epoch which is the number of epochs, the maximum memory max_memory which is the maximum number of trials kept in memory and the data_size which is the number of samples in training epoch. This is the number of trials randomly selected from the mouse’s experience

# This is a small utility for printing readable time strings:
def format_time(seconds):
    if seconds < 400:
        s = float(seconds)
        return "%.1f seconds" % (s,)
    elif seconds < 4000:
        m = seconds / 60.0
        return "%.2f minutes" % (m,)
    else:
        h = seconds / 3600.0
        return "%.2f hours" % (h,)

class Experiment(object):
    def __init__(self, maze=generate_random_maze(), model_learning_rate: int = 0.001):
        qmaze = Qmaze(maze)
        model = Model(maze, learning_rate=model_learning_rate)
        trial = Trial(model, qmaze, (0, 0))
        self.qmaze = qmaze
        self.model = model
        self.trial = trial

    def train(self, **opt):
        show(self.qmaze)
        print("training....")
        global epsilon
        number_epoch = opt.get("epochs", 15000)
        max_memory = opt.get("max_memory", 8)
        data_size = opt.get("data_size", 25)
        name = opt.get("name", "model")
        start_time = datetime.datetime.now()

        # Initialize experience replay object
        experience = Experience(self.model, max_memory=max_memory)

        completion_history = []  # history of win/lose game
        number_free_cells = len(self.qmaze.free_cells)
        hsize = self.qmaze.maze.size // 2  # history window size
        win_rate = 0.0
        imctr = 1

        for epoch in range(number_epoch):
            loss = 0.0
            mouse_cell = random.choice(self.qmaze.free_cells)
            self.qmaze.reset(mouse_cell)
            trial_over = False

            # get initial env_state (1d flattened canvas)
            env_state = self.qmaze.observe()

            n_trials = 0
            while not trial_over:
                valid_actions = self.qmaze.valid_actions()
                if not valid_actions:
                    break
                prev_env_state = env_state
                # Get next action
                if np.random.rand() < epsilon:
                    action = random.choice(valid_actions)
                else:
                    action = np.argmax(experience.predict(prev_env_state))

                # Apply action, get reward and new env_state
                env_state, reward, status = self.qmaze.act(action)
                if status == "win":
                    completion_history.append(1)
                    trial_over = True
                elif status == "lose":
                    completion_history.append(0)
                    trial_over = True
                else:
                    trial_over = False

                # Store trial (experience)
                trial = [prev_env_state, action, reward, env_state, trial_over]
                experience.remember(trial)
                n_trials += 1

                # Train neural network model
                inputs, targets = experience.data(data_size=data_size)
                self.model.model.fit(
                    inputs,
                    targets,
                    epochs=1,
                    batch_size=16,
                )
                loss = self.model.model.evaluate(inputs, targets, verbose=0)

            if len(completion_history) > hsize:
                win_rate = sum(completion_history[-hsize:]) / hsize

            dt = datetime.datetime.now() - start_time
            t = format_time(dt.total_seconds())
            template = "Epoch: {:03d}/{:d} | Loss: {:.4f} | Trials: {:d} | Win count: {:d} | Win rate: {:.3f} | time: {}"
            print(
                template.format(
                    epoch,
                    number_epoch - 1,
                    loss,
                    n_trials,
                    sum(completion_history),
                    win_rate,
                    t,
                )
            )
            # we simply check if training has exhausted all free cells and if in all
            # cases the agent won
            if win_rate > 0.9:
                epsilon = 0.05

            if sum(completion_history[-hsize:]) == hsize:  # and self.trial.check():
                print("Reached 100%% win rate at epoch: %d" % (epoch,))
                break

        # Save trained model weights and architecture, this will be used by the visualization code
        h5file = name + ".h5"
        json_file = name + ".json"
        self.model.get_model().save_weights(h5file, overwrite=True)
        with open(json_file, "w") as outfile:
            json.dump(self.model.get_model().to_json(), outfile)
        end_time = datetime.datetime.now()
        dt = datetime.datetime.now() - start_time
        seconds = dt.total_seconds()
        t = format_time(seconds)
        print("files: %s, %s" % (h5file, json_file))
        print(
            "n_epoch: %d, max_mem: %d, data: %d, time: %s"
            % (epoch, max_memory, data_size, t)
        )
        return seconds

Train the Model 

experiment = Experiment()

experiment.train()


training....
1/1 [==============================] - 0s 63ms/step
1/1 [==============================] - 0s 19ms/step
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1/1 [==============================] - 1s 861ms/step - loss: 0.0168
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Epoch: 000/14999 | Loss: 0.0113 | Trials: 36 | Win count: 0 | Win rate: 0.000 | time: 32.1 seconds
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Epoch: 001/14999 | Loss: 0.0109 | Trials: 35 | Win count: 0 | Win rate: 0.000 | time: 65.4 seconds
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Epoch: 002/14999 | Loss: 0.0244 | Trials: 35 | Win count: 0 | Win rate: 0.000 | time: 104.2 seconds
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Epoch: 003/14999 | Loss: 0.0865 | Trials: 34 | Win count: 0 | Win rate: 0.000 | time: 145.5 seconds
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Epoch: 004/14999 | Loss: 0.3342 | Trials: 6 | Win count: 1 | Win rate: 0.000 | time: 152.8 seconds
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Epoch: 005/14999 | Loss: 0.0696 | Trials: 33 | Win count: 1 | Win rate: 0.000 | time: 194.6 seconds
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1/1 [==============================] - 0s 4ms/step - loss: 0.1583
Epoch: 006/14999 | Loss: 0.1585 | Trials: 36 | Win count: 1 | Win rate: 0.000 | time: 235.6 seconds
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Epoch: 007/14999 | Loss: 0.3145 | Trials: 8 | Win count: 2 | Win rate: 0.000 | time: 244.1 seconds
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Epoch: 008/14999 | Loss: 0.0636 | Trials: 36 | Win count: 2 | Win rate: 0.250 | time: 283.8 seconds
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Epoch: 009/14999 | Loss: 0.1285 | Trials: 36 | Win count: 2 | Win rate: 0.250 | time: 325.6 seconds
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Epoch: 010/14999 | Loss: 0.1562 | Trials: 36 | Win count: 2 | Win rate: 0.250 | time: 369.7 seconds
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Epoch: 011/14999 | Loss: 0.1628 | Trials: 33 | Win count: 2 | Win rate: 0.250 | time: 6.83 minutes
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Epoch: 012/14999 | Loss: 0.1908 | Trials: 34 | Win count: 2 | Win rate: 0.125 | time: 7.60 minutes
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Epoch: 013/14999 | Loss: 0.1396 | Trials: 36 | Win count: 2 | Win rate: 0.125 | time: 8.47 minutes
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Epoch: 014/14999 | Loss: 0.1542 | Trials: 36 | Win count: 2 | Win rate: 0.125 | time: 9.30 minutes
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Epoch: 015/14999 | Loss: 0.1582 | Trials: 35 | Win count: 2 | Win rate: 0.000 | time: 10.02 minutes
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Epoch: 016/14999 | Loss: 0.1755 | Trials: 33 | Win count: 2 | Win rate: 0.000 | time: 10.72 minutes
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Epoch: 017/14999 | Loss: 0.1970 | Trials: 35 | Win count: 2 | Win rate: 0.000 | time: 11.47 minutes
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Epoch: 018/14999 | Loss: 0.1585 | Trials: 34 | Win count: 2 | Win rate: 0.000 | time: 12.30 minutes
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Epoch: 019/14999 | Loss: 0.3757 | Trials: 16 | Win count: 3 | Win rate: 0.125 | time: 12.71 minutes
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Epoch: 020/14999 | Loss: 0.1002 | Trials: 34 | Win count: 3 | Win rate: 0.125 | time: 13.47 minutes
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Epoch: 021/14999 | Loss: 0.1622 | Trials: 34 | Win count: 3 | Win rate: 0.125 | time: 14.32 minutes
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Epoch: 022/14999 | Loss: 0.1602 | Trials: 33 | Win count: 3 | Win rate: 0.125 | time: 15.04 minutes
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Epoch: 023/14999 | Loss: 0.5449 | Trials: 1 | Win count: 4 | Win rate: 0.250 | time: 15.06 minutes
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Epoch: 024/14999 | Loss: 0.7824 | Trials: 4 | Win count: 5 | Win rate: 0.375 | time: 15.14 minutes
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Epoch: 025/14999 | Loss: 0.0327 | Trials: 34 | Win count: 5 | Win rate: 0.375 | time: 15.88 minutes
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Epoch: 026/14999 | Loss: 0.0458 | Trials: 35 | Win count: 5 | Win rate: 0.375 | time: 16.91 minutes
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Epoch: 027/14999 | Loss: 0.0824 | Trials: 36 | Win count: 5 | Win rate: 0.250 | time: 17.99 minutes
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Epoch: 028/14999 | Loss: 0.1113 | Trials: 37 | Win count: 5 | Win rate: 0.250 | time: 19.14 minutes
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Epoch: 029/14999 | Loss: 0.4141 | Trials: 1 | Win count: 6 | Win rate: 0.375 | time: 19.18 minutes
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Epoch: 030/14999 | Loss: 0.0733 | Trials: 35 | Win count: 6 | Win rate: 0.375 | time: 20.37 minutes
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Epoch: 031/14999 | Loss: 0.1234 | Trials: 35 | Win count: 6 | Win rate: 0.250 | time: 21.50 minutes
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Epoch: 032/14999 | Loss: 0.1727 | Trials: 33 | Win count: 6 | Win rate: 0.125 | time: 22.59 minutes
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Epoch: 033/14999 | Loss: 0.2114 | Trials: 36 | Win count: 6 | Win rate: 0.125 | time: 23.76 minutes
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Epoch: 034/14999 | Loss: 0.1830 | Trials: 38 | Win count: 6 | Win rate: 0.125 | time: 24.85 minutes
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Epoch: 035/14999 | Loss: 0.1513 | Trials: 35 | Win count: 6 | Win rate: 0.125 | time: 26.01 minutes
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Epoch: 036/14999 | Loss: 0.1958 | Trials: 37 | Win count: 6 | Win rate: 0.125 | time: 27.31 minutes
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Epoch: 037/14999 | Loss: 0.1473 | Trials: 34 | Win count: 6 | Win rate: 0.000 | time: 28.85 minutes
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Epoch: 038/14999 | Loss: 0.1542 | Trials: 34 | Win count: 6 | Win rate: 0.000 | time: 30.38 minutes
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Epoch: 039/14999 | Loss: 0.1966 | Trials: 36 | Win count: 6 | Win rate: 0.000 | time: 31.86 minutes
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Epoch: 040/14999 | Loss: 0.1836 | Trials: 36 | Win count: 6 | Win rate: 0.000 | time: 33.43 minutes
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Epoch: 041/14999 | Loss: 0.1752 | Trials: 36 | Win count: 6 | Win rate: 0.000 | time: 34.99 minutes
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Epoch: 042/14999 | Loss: 0.1953 | Trials: 35 | Win count: 6 | Win rate: 0.000 | time: 36.46 minutes
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Epoch: 043/14999 | Loss: 0.3811 | Trials: 11 | Win count: 7 | Win rate: 0.125 | time: 36.89 minutes
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Epoch: 044/14999 | Loss: 0.6950 | Trials: 3 | Win count: 8 | Win rate: 0.250 | time: 36.99 minutes
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Epoch: 045/14999 | Loss: 0.0501 | Trials: 35 | Win count: 8 | Win rate: 0.250 | time: 38.38 minutes
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Epoch: 046/14999 | Loss: 0.2316 | Trials: 1 | Win count: 9 | Win rate: 0.375 | time: 38.41 minutes
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KeyboardInterrupt                         Traceback (most recent call last)

/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb Cell 19 in <cell line: 3>()
      <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=0'>1</a> experiment = Experiment()
----> <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=2'>3</a> experiment.train()


/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb Cell 19 in Experiment.train(self, **opt)
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=63'>64</a> n_trials += 1
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=65'>66</a> # Train neural network model
---> <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=66'>67</a> inputs, targets = experience.data(data_size=data_size)
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=67'>68</a> self.model.model.fit(
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=68'>69</a>     inputs,
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=69'>70</a>     targets,
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=70'>71</a>     epochs=1,
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=71'>72</a>     batch_size=16,
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=72'>73</a> )
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=73'>74</a> loss = self.model.model.evaluate(inputs, targets, verbose=0)


/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb Cell 19 in Experience.data(self, data_size)
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=29'>30</a> targets[idx] = self.predict(env_state)
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=30'>31</a> # Q_sa = derived policy = max quality env/action = max_a' Q(s', a')
---> <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=31'>32</a> Q_sa = np.max(self.predict(env_state_next))
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=32'>33</a> if trial_over:
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=33'>34</a>     targets[idx, action] = reward


/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb Cell 19 in Experience.predict(self, env_state)
     <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=14'>15</a> def predict(self, env_state):
---> <a href='vscode-notebook-cell:/Users/sammyrobens-paradise/projects/computational-neuroscience/project/reinforcement-mouse-learning.ipynb#X24sZmlsZQ%3D%3D?line=15'>16</a>     return self.model.get_model().predict(env_state)[0]


File /usr/local/lib/python3.9/site-packages/keras/utils/traceback_utils.py:65, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     63 filtered_tb = None
     64 try:
---> 65     return fn(*args, **kwargs)
     66 except Exception as e:
     67     filtered_tb = _process_traceback_frames(e.__traceback__)


File /usr/local/lib/python3.9/site-packages/keras/engine/training.py:2378, in Model.predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
   2376 callbacks.on_predict_begin()
   2377 batch_outputs = None
-> 2378 for _, iterator in data_handler.enumerate_epochs():  # Single epoch.
   2379     with data_handler.catch_stop_iteration():
   2380         for step in data_handler.steps():


File /usr/local/lib/python3.9/site-packages/keras/engine/data_adapter.py:1305, in DataHandler.enumerate_epochs(self)
   1303 """Yields `(epoch, tf.data.Iterator)`."""
   1304 with self._truncate_execution_to_epoch():
-> 1305     data_iterator = iter(self._dataset)
   1306     for epoch in range(self._initial_epoch, self._epochs):
   1307         if self._insufficient_data:  # Set by `catch_stop_iteration`.


File /usr/local/lib/python3.9/site-packages/tensorflow/python/data/ops/dataset_ops.py:505, in DatasetV2.__iter__(self)
    503 if context.executing_eagerly() or ops.inside_function():
    504   with ops.colocate_with(self._variant_tensor):
--> 505     return iterator_ops.OwnedIterator(self)
    506 else:
    507   raise RuntimeError("`tf.data.Dataset` only supports Python-style "
    508                      "iteration in eager mode or within tf.function.")


File /usr/local/lib/python3.9/site-packages/tensorflow/python/data/ops/iterator_ops.py:713, in OwnedIterator.__init__(self, dataset, components, element_spec)
    709   if (components is not None or element_spec is not None):
    710     raise ValueError(
    711         "When `dataset` is provided, `element_spec` and `components` must "
    712         "not be specified.")
--> 713   self._create_iterator(dataset)
    715 self._get_next_call_count = 0


File /usr/local/lib/python3.9/site-packages/tensorflow/python/data/ops/iterator_ops.py:752, in OwnedIterator._create_iterator(self, dataset)
    749   assert len(fulltype.args[0].args[0].args) == len(
    750       self._flat_output_types)
    751   self._iterator_resource.op.experimental_set_type(fulltype)
--> 752 gen_dataset_ops.make_iterator(ds_variant, self._iterator_resource)


File /usr/local/lib/python3.9/site-packages/tensorflow/python/ops/gen_dataset_ops.py:3408, in make_iterator(dataset, iterator, name)
   3406 if tld.is_eager:
   3407   try:
-> 3408     _result = pywrap_tfe.TFE_Py_FastPathExecute(
   3409       _ctx, "MakeIterator", name, dataset, iterator)
   3410     return _result
   3411   except _core._NotOkStatusException as e:


KeyboardInterrupt:

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