2022_01_PolicyGradientRL.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": "# Policy Gradient Reinforcement Learning",
   "metadata": {
    "tags": [],
    "cell_id": "f6475667-d496-4f67-8380-db3880b9af5d",
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   "source": "## General Overview\n\nThis notebook demonstrates a simple but general implementation of policy gradient reinforcement learning (for discrete actions).\n\nThe code is split into:\n\nPolicyGradient -- a class that implements the learning algorithm itself\n            and will make use of an 'agent' and an 'environment' that\n            are defined elsewhere. This is general and can be used for\n            any kind of (discrete-action) reinforcement learning task.\n\nQubitEnvironment -- a class that implements the environment for\n            a specific task (see below). The interface to this\n            class (i.e. the methods) is general and can also be\n            used for other tasks.\n\n...plus code that sets up the agent as a network and code that actually runs the training and visualizes the results.\n\nTwo noteworthy aspects here are: the use of batches (i.e. many trajectories can be processed in parallel, adding an extra dimension to every array, like states and actions and rewards) and the function ‘sample’ that selects a random action given a set of probabilities (provided by the policy). The latter is a bit involved since we try to deal with batches.\n\n## Physics Scenario\n\nThe particular scenario we consider here is inspired by quantum physics. Consider a spin and represent it by a single unit vector (the so-called Bloch vector). Imagine this vector starts out pointing in some (possibly random) direction, and our goal is to implement a series of rotations such that in the end the vector aligns as closely as possible with some target direction.\n\nAssume that only two different rotations can be applied (e.g. a rotation around the x-axis by some angle phi_x and another rotation around the z-axis, by some angle phi_z). Can the agent find a strategy that rotates a random initial vector in a fixed number of steps into the desired direction?\n\nThe state is given by the current unit vector (Bloch vector) and there are only two actions (a=0,1) corresponding to the two possible rotations. The reward is supplied only at the final time and is essentially the scalar product between the Bloch vector at the final time and the target vector.\n\n(C) 2022 Tirth Shah and Florian Marquardt, Theory Division at the Max Planck Institute for the Science of Light. MIT License.",
   "metadata": {
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    "cell_id": "00001-8cc7d3cd-040c-46e6-9a31-51aed6e12738",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
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    "execution_millis": 7430,
    "execution_start": 1642883763935,
    "source_hash": "1ab5ea93",
    "tags": [],
    "cell_id": "00002-b20a4b4e-1cc5-40f2-96b6-8e2eb3397591",
    "deepnote_cell_type": "code"
   },
   "source": "import numpy as np\nimport tensorflow as tf\nimport tensorflow.keras as tfk\nfrom tqdm import trange\nimport matplotlib.pyplot as plt",
   "execution_count": 1,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Brief reminder of policy gradient reinforcement learning, illustrated for this example\n\nstate s  = qubit state (pure)\n\n      s = Bloch vector = (n_x,n_y,n_z) where n = <Psi|sigma_vector|Psi>\n \nNote the *observed* state, passed to the agent, is a 4D vector, where the last entry is the current time step index. This allows the agent to react differently at the beginning of the trajectory and later.\n\n\nActions\n\n      actions = rotations of the qubit around two possible axes, and for each axis a fixed angle\n\n\nPolicy\n\n      policy = pi(a|s) = action probabilities\n\nReward and return\n\n      reward = (only final reward) = |<Psi(T)|Psi_target>| = 0.5*(1+ n * n_target)\n\n      return R = cumulative rewards = final reward\n\n\nPolicy gradient method 'REINFORCE', in one line\n\n (parametrize policy, e.g. neural network)\n\n    d theta = eta sum_t E[ R * (d/dtheta) ln pi_theta(a_t|s_t) ]\n\n method:  run trajectories, record s_0,a_0,s_1,...,s_T and R, \n then apply this d theta update\n\n",
   "metadata": {
    "tags": [],
    "cell_id": "00003-16ecb1e5-9244-4ca7-9f3e-1cec0dd995a7",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "## The policy gradient RL learner class",
   "metadata": {
    "cell_id": "3fca46fe-f477-4322-92d1-760c0f13d12d",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "This is the general code that could be re-used for any other kind of scenario.",
   "metadata": {
    "cell_id": "88186328-1b61-4272-9a03-45784182ba03",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 6,
    "execution_start": 1642884763630,
    "source_hash": "1203bde6",
    "tags": [],
    "cell_id": "00004-de157fb1-e761-4e3f-8127-d1a1019d855b",
    "deepnote_cell_type": "code"
   },
   "source": "def sample(probs):\n    \"\"\"\n    A function that, given probability distributions probs\n    (of shape [batchsize,bins]) for discrete events, samples\n    from these distributions. It returns an integer array of shape [batchsize]\n    which contains the random integer numbers that are distributed\n    according to probs. That is, result[j] is distributed according to probs[j,:].\n    \"\"\"\n    # get cumulative distribution for each sample:\n    prob_cumsum=np.cumsum(probs,axis=-1)\n    # pick a random number between 0 and 1 for each sample:\n    random_number=np.random.uniform(size=probs.shape[:-1])\n    # return the selected index for each sample:\n    return np.argmax(random_number[...,None]<prob_cumsum,axis=-1)\n\nclass SimplePolicyGradient:\n    def __init__(self,batchsize,num_steps,agent,environment,learning_rate=1e-3):\n        \"\"\"\n        Set up a policy gradient reinforcement learner that can process\n        batches of states and actions in parallel.\n\n        batchsize:\n            The batchsize.\n\n        num_steps:\n            Number of time steps in a trajectory.\n\n        agent:\n            One has to be able to call agent(state), which acts on a batch\n            of states of shape [batchsize,...], where ... is defined by environment.\n            Returns a set of action probabilities, shape [batchsize,num_actions].\n            This could simply be a keras Model with the right input dimensions (defined\n            by the environment) and the right number of output neurons for the\n            desired number of actions, using softmax (compatible with environment).\n            Besides being able to act on a state and return the policy, it also\n            has to supply agent.trainable_variables (this will automatically be the\n            case for any keras model).\n        \n        environment:\n            Has to implement env.prepare_initial_state(batchsize),\n            env.provide_observed_state(), and env.perform_action(action,time_step,num_steps).\n            env.provide_observed_state() returns the current observed state (a batch), while\n            perform_action returns the reward (batch of rewards) after performing action.\n        \"\"\"\n        self.batchsize=batchsize\n        self.num_steps=num_steps\n        self.agent=agent\n        self.environment=environment\n        self.learning_rate = learning_rate\n        self.return_history = []\n        self.optimizer=tfk.optimizers.Adam(learning_rate=self.learning_rate)\n\n    def run_trajectory(self):\n        \"\"\"\n        Run a single trajectory and update self.return_R and\n        self.state_history and self.action_history.\n        \"\"\"\n        self.environment.prepare_initial_state(self.batchsize)\n        self.return_R=np.zeros([self.batchsize])\n        for time_step in range(self.num_steps):\n            state=self.environment.provide_observed_state()\n            action_probabilities=self.agent(state) # shape [batchsize,num_actions]\n            action=sample(action_probabilities) # shape [batchsize]\n            reward=self.environment.perform_action(action,time_step,num_steps)\n            self.return_R += reward\n            if time_step==0:\n                self.state_history = np.zeros([self.num_steps]+list(state.shape))\n                self.action_history = np.zeros([self.num_steps, self.batchsize],dtype='int')\n            self.state_history[time_step,...]=state\n            self.action_history[time_step,:]=action\n\n    def update_policy_gradient(self):\n        \"\"\"\n        Call this after having run a trajectory. This uses the\n        cumulative reward that was recorded, together with the\n        history of states and actions, to update the policy of the agent.\n        \"\"\"\n        idx=np.arange(self.batchsize)\n\n        for time_step in range(self.num_steps):\n            with tf.GradientTape() as tape:\n                # now calculate R * ln pi(a_t|s_t), within the gradient tape\n\n                # evaluate policy probabilities at this time step (a full batch of prob. distributions)\n                action_probs = agent(self.state_history[time_step,...])\n                # now fill an array of the same shape, inserting the return R at only\n                # the slot of the action that was actually performed, for each sample in the batch:\n                selected_actions=np.zeros(action_probs.shape)\n                selected_actions[idx,self.action_history[time_step,:]]=-self.return_R\n                # Now, for the loss, calculate - R * ln pi(a_t|s_t) for the\n                # selected actions, averaged over the batch!\n                # Note: We used the - sign  above because the loss will be *minimized*.\n                # Note: this is really a categorical cross-entropy, except we multiplied with R for\n                # each sample!\n                loss = tf.reduce_mean(selected_actions * tf.math.log(action_probs))\n            # get the gradient that is needed for policy gradient\n            grads = tape.gradient(loss, agent.trainable_variables)\n            # applying the gradient immediately involves a certain approximation,\n            # namely the policy will now already have changed a bit when we go to\n            # evaluate it in the next time step. Nevertheless, this keeps things\n            # simple. Should not matter too much if learning rate is small enough.\n            self.optimizer.apply_gradients(zip(grads,agent.trainable_variables))\n                \n    def train(self,train_steps):\n        for step in trange(train_steps):\n            self.run_trajectory()\n            self.update_policy_gradient()\n            self.return_history.append(np.mean(self.return_R))\n\n\n        ",
   "execution_count": 13,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## The physics environment (a qubit)",
   "metadata": {
    "cell_id": "e207db08-ac4d-46b9-92b7-c07922113829",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "source_hash": "9e4be6c5",
    "execution_start": 1642884894418,
    "execution_millis": 26,
    "cell_id": "00005-df8c8a2f-4090-4025-8e1b-c106b2a265b7",
    "deepnote_cell_type": "code"
   },
   "source": "def rotation_matrix(rot_axis, rot_angle):\n    '''\n    Rotation matrix about the axis x,y,z (rot_axis=0,1,2) for \n    angle rot_angle (in radians)\n    '''\n\n    rot_matrix = np.zeros([3,3])\n    rot_matrix[rot_axis,rot_axis] = 1\n    rot_matrix[(rot_axis+1)%3,(rot_axis+1)%3] = np.cos(rot_angle)\n    rot_matrix[(rot_axis+1)%3,(rot_axis+2)%3] = -np.sin(rot_angle)\n    rot_matrix[(rot_axis+2)%3,(rot_axis+1)%3] = np.sin(rot_angle)\n    rot_matrix[(rot_axis+2)%3,(rot_axis+2)%3] = np.cos(rot_angle)\n\n    return rot_matrix\n\nclass QubitEnvironment:\n    \"\"\"\n    All the call interfaces here are general, except for the init method\n    (and of course except for the 'overlap' method).\n    \"\"\"\n    def __init__(self,action_matrix,target_state,num_actions,state_dimension,\n                reward_all=False):\n        \"\"\"\n        Set up parameters of the environment.\n        \"\"\"\n        self.action_matrix = action_matrix\n        self.num_actions = num_actions\n        self.state_dimension = state_dimension\n        self.reward_all = reward_all\n        self.target_state = target_state\n\n    def prepare_initial_state(self,batchsize):\n        \"\"\"\n        Prepare internally the state at the starting point of\n        a trajectory (actually, a batch of states, given by batchsize).\n        \"\"\"\n        # last dimension in the state corresponds to the Bloch vector = (n_x,n_y,n_z)\n        self.state = np.random.randn(batchsize,self.state_dimension)\n        self.state = self.state/(np.linalg.norm(self.state, axis=-1)[:,None])\n        self.current_time_step=0\n        self.batchsize=batchsize\n\n    def provide_observed_state(self):\n        \"\"\"\n        Return the current observed state, to be given to the agent. (actually, a batch of states, of\n        some shape [batchsize,...]).\n        \"\"\"\n        observed_state=np.zeros([self.batchsize,self.state_dimension+1])\n        # now include the time index in the state that is observed by the agent:\n        observed_state[:,:-1]=self.state\n        observed_state[:,-1]=self.current_time_step\n        return observed_state\n\n    def perform_action(self,action,time_step,num_steps):\n        \"\"\"\n        Perform actions 'action' (shape: [batchsize]) in parallel\n        on a batch, updating the internal state, and return an array\n        of rewards (shape: [batchsize]).\n\n        time_step: current time step (0 will be first)\n        num_step:  maximum number of time_steps (time_step == num_steps-1 will indicate last)\n\n        Here the actions refer to the application of different rotations,\n        which are provided as matrices when setting up the environment.\n        \"\"\"\n        \n        self.current_time_step = time_step\n        self.state = np.matmul(self.action_matrix[action],self.state[...,None])[...,0]\n\n        if self.reward_all: # reward is evaluated at all the time steps\n            return self.overlap(self.state,self.target_state)\n        else: # reward is evaluated only at the final time step\n            if time_step==num_steps-1:\n                return self.overlap(self.state,self.target_state)\n            else:\n                return 0\n        \n    def overlap(self,state,target_state):\n        '''\n        Evaluate the overlap of the state and the target state.\n        Overlap = 0.5*(1+ n * n_target)\n        \n        state is of the shape [:,state_dimension]\n        target_state is of the shape [state_dimension]\n        '''\n        dot_product = np.sum(state*target_state[None,:], axis=-1)\n        return 0.5*(1 + dot_product) # reward = 0.5*(1+ n * n_target)",
   "execution_count": 30,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "## Some small tests for the probabilistic selection of actions",
   "metadata": {
    "cell_id": "7f9697c9-2167-4d37-bd2d-071c7122465d",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "Test the sample function (sampling according to given probability distribution)",
   "metadata": {
    "cell_id": "00006-e0ab1fcc-0faf-4af0-9b18-16be396958e5",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_output_heights": [
     21.1875
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 17,
    "execution_start": 1642884896248,
    "scrolled": true,
    "source_hash": "40db8f36",
    "tags": [],
    "cell_id": "00007-9a0adc85-09b5-4059-83d9-f6ec4c9edeb6",
    "deepnote_cell_type": "code"
   },
   "source": "# little toy example of generating numbers\n# according to a given probability distribution\nprobs=[0.3,0.5,0.2]\nprobs_cum=np.cumsum(probs)\ncounter=np.zeros(3)\nfor _ in range(1000):\n    x=np.random.uniform()\n    result=np.where(x<probs_cum)[0][0]\n    counter[result]+=1\n# print frequencies:\nprint(\"Observed:\", counter/1000)\nprint(\"Expected:\", probs)",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "text": "Observed: [0.317 0.477 0.206]\nExpected: [0.3, 0.5, 0.2]\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "source_hash": "85dbda2f",
    "execution_start": 1642884897061,
    "execution_millis": 89,
    "cell_id": "00008-130b38b9-44f2-4f36-af21-20ea73651568",
    "deepnote_cell_type": "code"
   },
   "source": "# Checking the batch-enabled sample function that is used by us:\n\nprobs=np.array([[0.3,0.5,0.2],[0.7,0.2,0.1]]) # shape [2,3]\n\ncounter=np.zeros(probs.shape)\nfor _ in range(1000):\n    result = sample(probs)    \n    for sample_idx in range(probs.shape[0]):\n        counter[sample_idx,result[sample_idx]]+=1\n    \nprint(\"Observed:\", counter/1000)\nprint(\"Expected:\", probs)",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "text": "Observed: [[0.306 0.484 0.21 ]\n [0.68  0.207 0.113]]\nExpected: [[0.3 0.5 0.2]\n [0.7 0.2 0.1]]\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Perform the training",
   "metadata": {
    "cell_id": "00009-d5baefce-85b9-4339-a511-6bd1e69e3a28",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Initialize physics (environment) properties",
   "metadata": {
    "cell_id": "cbd5161b-c230-45ae-9f03-ca3c7f45da9d",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_output_heights": [
     59.5625
    ],
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 13,
    "execution_start": 1642884899216,
    "source_hash": "5fa6d273",
    "tags": [],
    "cell_id": "00010-62cb494b-2143-4891-955c-63997677f0d7",
    "deepnote_cell_type": "code"
   },
   "source": "# Selecting the two rotation axes and the corresponding angles\n# here you could choose different axes and angles.\nrot_axis = [0,2] # rotation axis (0,1,2) -> (x,y,z) axis\nrot_angle = [np.pi/8, np.pi/8] # angle of rotation about the two axes\n\n# Obtain the action matrices (2 rotation matrices and an identity matrix)\nnum_actions = 3\naction_matrix = np.zeros([num_actions,3,3]) # shape [num_action matrices, xyz, xyz]\naction_matrix[0,...] = rotation_matrix(rot_axis=rot_axis[0], rot_angle=rot_angle[0])\naction_matrix[1,...] = rotation_matrix(rot_axis=rot_axis[1], rot_angle=rot_angle[1])\naction_matrix[2,...] = rotation_matrix(rot_axis=0, rot_angle=0)\n\n\nstate_dimension = 3 # Bloch vector's 3 dimensions\ntarget_state = np.array([2.5,0.6,1]) # Bloch vector of the target state\ntarget_state = target_state/np.linalg.norm(target_state) # normalization\n\n# Environment\n# reward_all=True -> return is the sum of overlaps at all the time steps\n# reward_all=False -> return is the overlap at the final time step\nenvironment = QubitEnvironment(action_matrix,target_state=target_state,\n                  num_actions=num_actions,state_dimension=state_dimension,\n                  reward_all=False)",
   "execution_count": 33,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### Initialize agent and RL policy gradient learner",
   "metadata": {
    "cell_id": "f81d770f-03ad-4d09-ba1a-35b33f8c0128",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "deepnote_to_be_reexecuted": false,
    "source_hash": "6c953c02",
    "execution_start": 1642884900646,
    "execution_millis": 44,
    "cell_id": "00011-de4bd498-9fe5-40b7-8bf3-cb5d8ddbfa65",
    "deepnote_cell_type": "code"
   },
   "source": "# Agent policy network\n\n# Initialize the policy neural network that returnsthe actin probabilities: pi(a_t|s_t).\nlayers = [10,10] # List containing the number of neurons in each hidden layer\nN_in = state_dimension+1 # number of neurons in the input layer (state_dimension + time_step)\nN_out = num_actions # number of neurons in the output layer\n       \nDense = tfk.layers.Dense\nagent = tfk.Sequential([Dense(layers[0], activation='relu', input_shape=(N_in,))] + \n                     [Dense(n, activation='relu') for n in layers[1:]] +\n                     [Dense(N_out, activation='softmax')]\n                    )\n\n# Learner\n\nbatchsize = 32\nnum_steps = 10 # maximum number of time steps during each run\n\nlearner = SimplePolicyGradient(batchsize,num_steps,agent,environment,learning_rate=1e-3) # Define the RL policy",
   "execution_count": 34,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": "### The actual training",
   "metadata": {
    "cell_id": "ef52d958-1f7d-49b5-96a4-b24424c57960",
    "tags": [],
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": false,
    "deepnote_to_be_reexecuted": false,
    "source_hash": "56ec683e",
    "execution_start": 1642884902878,
    "execution_millis": 22005,
    "deepnote_output_heights": [
     null,
     386
    ],
    "cell_id": "00012-2c616a1b-ed09-4069-b0b3-2bda8d3e9ebe",
    "deepnote_cell_type": "code"
   },
   "source": "train_steps = 200\n\nlearner.train(train_steps=train_steps) # Perform the training",
   "execution_count": 35,
   "outputs": [
    {
     "name": "stderr",
     "text": "100%|██████████| 200/200 [00:21<00:00,  9.10it/s]\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "## Visualization of the results",
   "metadata": {
    "cell_id": "00013-9a6c5086-e742-4e7a-8c62-f237905a0424",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "markdown",
   "source": "### Average return vs training step",
   "metadata": {
    "cell_id": "00014-0e5f1180-0f6f-4389-b7d5-1635a846ad85",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": false,
    "deepnote_to_be_reexecuted": false,
    "source_hash": "c46c86f1",
    "execution_start": 1642884926695,
    "execution_millis": 630,
    "deepnote_output_heights": [
     605
    ],
    "cell_id": "00015-61908603-6b91-4dc3-ab39-1d6fa2210b05",
    "deepnote_cell_type": "code"
   },
   "source": "# If reward is evaluated only at the final time step, then \n# the y-axis corresponds to the overlap of the final state \n# with the target state. Otherwise, the y-axis is the sum of the \n# overlaps at all the time steps.\n\nfig = plt.figure(dpi=300)\nax = plt.axes()\n\nax.plot(learner.return_history)\n\nax.set_xlabel('Training step')\nax.set_ylabel('Return')\n\nplt.show()",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1800x1200 with 1 Axes>",
      "image/png": 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     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 1609,
       "height": 1109
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": "### Test the network",
   "metadata": {
    "cell_id": "00016-60167be5-b504-4b8f-8b0e-f28d5ed765fc",
    "deepnote_cell_type": "markdown"
   }
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "source_hash": "d4b9db81",
    "execution_start": 1642884931121,
    "execution_millis": 30,
    "deepnote_output_heights": [
     21
    ],
    "cell_id": "00017-e6268624-87de-456c-a9fb-9d52c0523471",
    "deepnote_cell_type": "code"
   },
   "source": "# Run a batch of initial states\nlearner.run_trajectory()\nlearner.update_policy_gradient() # just for getting access to the return R\n\nprint(np.mean(learner.return_R))",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "text": "0.7808185281857567\n",
     "output_type": "stream"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "deepnote_to_be_reexecuted": false,
    "source_hash": "1268e941",
    "execution_start": 1642884957770,
    "execution_millis": 655,
    "deepnote_output_heights": [
     462,
     444
    ],
    "cell_id": "00019-5df6e315-a11f-4d09-84ee-4aa92d7533ad",
    "deepnote_cell_type": "code"
   },
   "source": "# investigate one particular case of the batch in detail\nbatch_idx = 14\n\nfig, ax = plt.subplots(nrows=2, dpi=150, figsize=(8,4))\n\n# Action operations vs time\n# y=n => action=n\nax[0].bar(np.arange(num_steps), learner.action_history[:,batch_idx]+1, bottom=-1)\nax[0].set_ylim(-0.5,num_actions+0.5)\n\nax[0].set_xticks([])\nax[0].set_ylabel('Action index')\n\n# Reward (overlap) vs time\noverlap = environment.overlap(learner.state_history[:,batch_idx,:3], target_state)\nax[1].plot(overlap)\nax[1].set_ylim(0.0,1)\n\nax[1].set_xlabel('Operation step')\nax[1].set_ylabel('Overlap')\n\nplt.subplots_adjust(hspace=0.0)\n\nplt.show()",
   "execution_count": 41,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x600 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 1036,
       "height": 545
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "deepnote_to_be_reexecuted": false,
    "source_hash": "14813636",
    "execution_start": 1642884963788,
    "execution_millis": 1729,
    "deepnote_output_heights": [
     611,
     611
    ],
    "cell_id": "00020-913435a2-5e1f-4cd1-ab27-06aca6a34ade",
    "deepnote_cell_type": "code"
   },
   "source": "# investigate the overlaps of many cases in the batch\nN_cases = 10\nbatch_idx = np.arange(N_cases)\n\nfig, ax = plt.subplots(nrows=N_cases, dpi=150, figsize=(8,6))\n\n# Overlap vs time\nfor idx in range(N_cases):\n    overlap = environment.overlap(learner.state_history[:,idx,:3], target_state)\n    ax[idx].plot(overlap)\n    ax[idx].set_ylim(0.0,1)\n\nax[N_cases-1].set_xlabel('Time step')\nax[0].set_ylabel('Overlap')\n\nplt.subplots_adjust(hspace=0.2)\n\nplt.show()",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x900 with 10 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light",
      "image/png": {
       "width": 1016,
       "height": 784
      }
     },
     "output_type": "display_data"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "tags": [],
    "cell_id": "00021-64f96fe1-2f39-41c5-b620-25bf17c7e761",
    "deepnote_to_be_reexecuted": false,
    "source_hash": "ae3f48eb",
    "execution_start": 1642884983925,
    "execution_millis": 4,
    "deepnote_cell_type": "code"
   },
   "source": "# This can still be improved by further training...",
   "execution_count": 43,
   "outputs": []
  },
  {
   "cell_type": "markdown",
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