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Tutorial 1: Learning to Predict

Week 3, Day 4: Reinforcement Learning

By Neuromatch Academy

Content creators: Marcelo Mattar and Eric DeWitt with help from Matt Krause

Content reviewers: Ella Batty, Byron Galbraith and Michael Waskom

Our 2021 Sponsors, including Presenting Sponsor Facebook Reality Labs

Tutorial objectives

Estimated timing of tutorial: 50 min

In this tutorial, we will learn how to estimate state-value functions in a classical conditioning paradigm using Temporal Difference (TD) learning and examine TD-errors at the presentation of the conditioned and unconditioned stimulus (CS and US) under different CS-US contingencies. These exercises will provide you with an understanding of both how reward prediction errors (RPEs) behave in classical conditioning and what we should expect to see if Dopamine represents a “canonical” model-free RPE.

At the end of this tutorial:

  • You will learn to use the standard tapped delay line conditioning model

  • You will understand how RPEs move to CS

  • You will understand how variability in reward size effects RPEs

  • You will understand how differences in US-CS timing effect RPEs

Tutorial slides

These are the slides for all videos in this tutorial.


# Imports

import numpy as np
import matplotlib.pyplot as plt

Figure Settings

# @title Figure Settings
import ipywidgets as widgets       # interactive display
%config InlineBackend.figure_format = 'retina'"")

Plotting Functions

# @title Plotting Functions
from matplotlib import ticker

def plot_value_function(V, ax=None, show=True):
  """Plot V(s), the value function"""
  if not ax:
    fig, ax = plt.subplots()

  ax.stem(V, use_line_collection=True)
  ax.set_title("Value function: $V(s)$")

  if show:

def plot_tde_trace(TDE, ax=None, show=True, skip=400):
  """Plot the TD Error across trials"""
  if not ax:
    fig, ax = plt.subplots()

  indx = np.arange(0, TDE.shape[1], skip)
  im = ax.imshow(TDE[:,indx])
  positions = ax.get_xticks()
  # Avoid warning when setting string tick labels
  ax.set_xticklabels([f"{int(skip * x)}" for x in positions])
  ax.set_title('TD-error over learning')
  if show:

def learning_summary_plot(V, TDE):
  """Summary plot for Ex1"""
  fig, (ax1, ax2) = plt.subplots(nrows = 2, gridspec_kw={'height_ratios': [1, 2]})

  plot_value_function(V, ax=ax1, show=False)
  plot_tde_trace(TDE, ax=ax2, show=False)

Helper Functions and Classes

# @title Helper Functions and Classes

def reward_guesser_title_hint(r1, r2):
  """"Provide a mildly obfuscated hint for a demo."""
  if (r1==14 and r2==6) or (r1==6 and r2==14):
    return "Technically correct...(the best kind of correct)"

  if  ~(~(r1+r2) ^ 11) - 1 == (6 | 24): # Don't spoil the fun :-)
    return "Congratulations! You solved it!"

  return "Keep trying...."

class ClassicalConditioning:

    def __init__(self, n_steps, reward_magnitude, reward_time):

        # Task variables
        self.n_steps = n_steps
        self.n_actions = 0
        self.cs_time = int(n_steps/4) - 1

        # Reward variables
        self.reward_state = [0,0]
        self.reward_magnitude = None
        self.reward_probability = None
        self.reward_time = None

        self.set_reward(reward_magnitude, reward_time)

        # Time step at which the conditioned stimulus is presented

        # Create a state dictionary

    def set_reward(self, reward_magnitude, reward_time):

        Determine reward state and magnitude of reward
        if reward_time >= self.n_steps - self.cs_time:
            self.reward_magnitude = 0

            self.reward_magnitude = reward_magnitude
            self.reward_state = [1, reward_time]

    def get_outcome(self, current_state):

        Determine next state and reward
        # Update state
        if current_state < self.n_steps - 1:
            next_state = current_state + 1
            next_state = 0

        # Check for reward
        if self.reward_state == self.state_dict[current_state]:
            reward = self.reward_magnitude
            reward = 0

        return next_state, reward

    def _create_state_dictionary(self):

        This dictionary maps number of time steps/ state identities
        in each episode to some useful state attributes:

        state      - 0 1 2 3 4 5 (cs) 6 7 8 9 10 11 12 ...
        is_delay   - 0 0 0 0 0 0 (cs) 1 1 1 1  1  1  1 ...
        t_in_delay - 0 0 0 0 0 0 (cs) 1 2 3 4  5  6  7 ...
        d = 0

        self.state_dict = {}
        for s in range(self.n_steps):
            if s <= self.cs_time:
                self.state_dict[s] = [0,0]
                d += 1 # Time in delay
                self.state_dict[s] = [1,d]

class MultiRewardCC(ClassicalConditioning):
  """Classical conditioning paradigm, except that one randomly selected reward,
    magnitude, from a list, is delivered of a single fixed reward."""
  def __init__(self, n_steps, reward_magnitudes, reward_time=None):
    """"Build a multi-reward classical conditioning environment
        - nsteps: Maximum number of steps
        - reward_magnitudes: LIST of possible reward magnitudes.
        - reward_time: Single fixed reward time
      Uses numpy global random state.
    super().__init__(n_steps, 1, reward_time)
    self.reward_magnitudes = reward_magnitudes

  def get_outcome(self, current_state):
    next_state, reward = super().get_outcome(current_state)
    if reward:
    return next_state, reward

class ProbabilisticCC(ClassicalConditioning):
  """Classical conditioning paradigm, except that rewards are stochastically omitted."""
  def __init__(self, n_steps, reward_magnitude, reward_time=None, p_reward=0.75):
    """"Build a multi-reward classical conditioning environment
        - nsteps: Maximum number of steps
        - reward_magnitudes: Reward magnitudes.
        - reward_time: Single fixed reward time.
        - p_reward: probability that reward is actually delivered in rewarding state
      Uses numpy global random state.
    super().__init__(n_steps, reward_magnitude, reward_time)
    self.p_reward = p_reward

  def get_outcome(self, current_state):
    next_state, reward = super().get_outcome(current_state)
    if reward:
      reward*= int(np.random.uniform(size=1)[0] < self.p_reward)
    return next_state, reward

Section 1: Temporal difference learning

Video 1: Introduction


  • The agent experiences the environment in episodes or trials.

  • Episodes terminate by transitioning to the inter-trial-interval (ITI) state and they are initiated from the ITI state as well. We clamp the value of the terminal/ITI states to zero.

  • The classical conditioning environment is composed of a sequence of states that the agent deterministically transitions through. Starting at State 0, the agent moves to State 1 in the first step, from State 1 to State 2 in the second, and so on. These states represent time in the tapped delay line representation

  • Within each episode, the agent is presented a CS and US (reward).

  • The CS is always presented at 1/4 of the total duration of the trial. The US (reward) is then delivered after the CS. The interval between the CS and US is specified by reward_time.

  • The agent’s goal is to learn to predict expected rewards from each state in the trial.

General concepts

  • Return \(G_{t}\): future cumulative reward, which can be written in a recursive form

(232)\[\begin{align} G_{t} &= \sum \limits_{k = 0}^{\infty} \gamma^{k} r_{t+k+1} \\ &= r_{t+1} + \gamma G_{t+1} \end{align}\]

where \(\gamma\) is discount factor that controls the importance of future rewards, and \(\gamma \in [0, 1]\). \(\gamma\) may also be interpreted as probability of continuing the trajectory.

  • Value funtion \(V_{\pi}(s_t=s)\): expecation of the return

(233)\[\begin{align} V_{\pi}(s_t=s) &= \mathbb{E} [ G_{t}\; | \; s_t=s, a_{t:\infty}\sim\pi] \\ & = \mathbb{E} [ r_{t+1} + \gamma G_{t+1}\; | \; s_t=s, a_{t:\infty}\sim\pi] \end{align}\]

With an assumption of Markov process, we thus have:

(234)\[\begin{align} V_{\pi}(s_t=s) &= \mathbb{E} [ r_{t+1} + \gamma V_{\pi}(s_{t+1})\; | \; s_t=s, a_{t:\infty}\sim\pi] \\ &= \sum_a \pi(a|s) \sum_{r, s'}p(s', r)(r + V_{\pi}(s_{t+1}=s')) \end{align}\]

Temporal difference (TD) learning

  • With a Markovian assumption, we can use \(V(s_{t+1})\) as an imperfect proxy for the true value \(G_{t+1}\) (Monte Carlo bootstrapping), and thus obtain the generalised equation to calculate TD-error:

(235)\[\begin{align} \delta_{t} = r_{t+1} + \gamma V(s_{t+1}) - V(s_{t}) \end{align}\]
  • Value updated by using the learning rate constant \(\alpha\):

(236)\[\begin{align} V(s_{t}) \leftarrow V(s_{t}) + \alpha \delta_{t} \end{align}\]



  • TD-error:

(237)\[\begin{align} \delta_{t} = r_{t+1} + \gamma V(s_{t+1}) - V(s_{t}) \end{align}\]
  • Value updates:

(238)\[\begin{align} V(s_{t}) \leftarrow V(s_{t}) + \alpha \delta_{t} \end{align}\]

Coding Exercise 1: TD-learning with guaranteed rewards

Implement TD-learning to estimate the state-value function in the classical-conditioning world with guaranteed rewards, with a fixed magnitude, at a fixed delay after the conditioned stimulus, CS. Save TD-errors over learning (i.e., over trials) so we can visualize them afterwards.

In order to simulate the effect of the CS, you should only update \(V(s_{t})\) during the delay period after CS. This period is indicated by the boolean variable is_delay. This can be implemented by multiplying the expression for updating the value function by is_delay.

Use the provided code to estimate the value function. We will use helper class ClassicalConditioning.

def td_learner(env, n_trials, gamma=0.98, alpha=0.001):
  """ Temporal Difference learning

    env (object): the environment to be learned
    n_trials (int): the number of trials to run
    gamma (float): temporal discount factor
    alpha (float): learning rate

    ndarray, ndarray: the value function and temporal difference error arrays
  V = np.zeros(env.n_steps) # Array to store values over states (time)
  TDE = np.zeros((env.n_steps, n_trials)) # Array to store TD errors

  for n in range(n_trials):

    state = 0 # Initial state

    for t in range(env.n_steps):

      # Get next state and next reward
      next_state, reward = env.get_outcome(state)

      # Is the current state in the delay period (after CS)?
      is_delay = env.state_dict[state][0]

      ## TODO for students: implement TD error and value function update
      # Fill out function and remove
      raise NotImplementedError("Student excercise: implement TD error and value function update")
      # Write an expression to compute the TD-error
      TDE[state, n] = ...

      # Write an expression to update the value function
      V[state] += ...

      # Update state
      state = next_state

  return V, TDE

# Initialize classical conditioning class
env = ClassicalConditioning(n_steps=40, reward_magnitude=10, reward_time=10)

# Perform temporal difference learning
V, TDE = td_learner(env, n_trials=20000)

# Visualize
learning_summary_plot(V, TDE)

Click for solution

Example output:

Solution hint

Interactive Demo 1.1: US to CS Transfer

During classical conditioning, the subject’s behavioral response (e.g., salivating) transfers from the unconditioned stimulus (US; like the smell of tasty food) to the conditioned stimulus (CS; like Pavlov ringing his bell) that predicts it. Reward prediction errors play an important role in this process by adjusting the value of states according to their expected, discounted return.

Use the widget below to examine how reward prediction errors change over time.

Before training (orange line), only the reward state has high reward prediction error. As training progresses (blue line, slider), the reward prediction errors shift to the conditioned stimulus, where they end up when the trial is complete (green line).

Dopamine neurons, which are thought to carry reward prediction errors in vivo, show exactly the same behavior!

Make sure you execute this cell to enable the widget!


#@markdown Make sure you execute this cell to enable the widget!

n_trials = 20000

def plot_tde_by_trial(trial = widgets.IntSlider(value=5000, min=0, max=n_trials-1 , step=1, description="Trial #")):
  if 'TDE' not in globals():
    print("Complete Exercise 1 to enable this interactive demo!")

    fig, ax = plt.subplots()
    ax.axhline(0, color='k') # Use this + basefmt=' ' to keep the legend clean.
    ax.stem(TDE[:, 0], linefmt='C1-', markerfmt='C1d', basefmt=' ',
            label="Before Learning (Trial 0)",
    ax.stem(TDE[:, -1], linefmt='C2-', markerfmt='C2s', basefmt=' ',
            label="After Learning (Trial $\infty$)",
    ax.stem(TDE[:, trial], linefmt='C0-', markerfmt='C0o', basefmt=' ',
            label=f"Trial {trial}",

    ax.set_xlabel("State in trial")
    ax.set_ylabel("TD Error")
    ax.set_title("Temporal Difference Error by Trial")

Interactive Demo 1.2: Learning Rates and Discount Factors

Our TD-learning agent has two parameters that control how it learns: \(\alpha\), the learning rate, and \(\gamma\), the discount factor. In Exercise 1, we set these parameters to \(\alpha=0.001\) and \(\gamma=0.98\) for you. Here, you’ll investigate how changing these parameters alters the model that TD-learning learns.

Before enabling the interactive demo below, take a moment to think about the functions of these two parameters. \(\alpha\) controls the size of the Value function updates produced by each TD-error. In our simple, deterministic world, will this affect the final model we learn? Is a larger \(\alpha\) necessarily better in more complex, realistic environments?

The discount rate \(\gamma\) applies an exponentially-decaying weight to returns occuring in the future, rather than the present timestep. How does this affect the model we learn? What happens when \(\gamma=0\) or \(\gamma \geq 1\)?

Use the widget to test your hypotheses.

Make sure you execute this cell to enable the widget!


#@markdown Make sure you execute this cell to enable the widget!

def plot_summary_alpha_gamma(alpha = widgets.FloatSlider(value=0.0001, min=0.0001, max=0.1, step=0.0001, readout_format='.4f', description="alpha"),
                             gamma = widgets.FloatSlider(value=0.980, min=0, max=1.1, step=0.010, description="gamma")):
  env = ClassicalConditioning(n_steps=40, reward_magnitude=10, reward_time=10)
    V_params, TDE_params = td_learner(env, n_trials=20000, gamma=gamma, alpha=alpha)
  except NotImplementedError:
    print("Finish Exercise 1 to enable this interactive demo")


Click for solution

Section 2: TD-learning with varying reward magnitudes

Estimated timing to here from start of tutorial: 30 min

In the previous exercise, the environment was as simple as possible. On every trial, the CS predicted the same reward, at the same time, with 100% certainty. In the next few exercises, we will make the environment more progressively more complicated and examine the TD-learner’s behavior.

Interactive Demo 2: Match the Value Functions

First, will replace the environment with one that dispenses one of several rewards, chosen at random. Shown below is the final value function \(V\) for a TD learner that was trained in an enviroment where the CS predicted a reward of 6 or 14 units; both rewards were equally likely).

Can you find another pair of rewards that cause the agent to learn the same value function? Assume each reward will be dispensed 50% of the time.


  • Carefully consider the definition of the value function \(V\). This can be solved analytically.

  • There is no need to change \(\alpha\) or \(\gamma\).

  • Due to the randomness, there may be a small amount of variation.

Make sure you execute this cell to enable the widget! Please allow some time for the new figure to load

# @markdown Make sure you execute this cell to enable the widget! Please allow some time for the new figure to load

n_trials = 20000
rng_state = np.random.get_state()
env = MultiRewardCC(40, [6, 14], reward_time=10)
V_multi, TDE_multi = td_learner(env, n_trials, gamma=0.98, alpha=0.001)

def reward_guesser_interaction(r1 = widgets.IntText(value=0, min=0, max=50, description="Reward 1"),
                               r2 = widgets.IntText(value=0, min=0, max=50, description="Reward 2")):
    env2 = MultiRewardCC(40, [r1, r2], reward_time=10)
    V_guess, _ = td_learner(env2, n_trials, gamma=0.98, alpha=0.001)
    fig, ax = plt.subplots()
    m, l, _ = ax.stem(V_multi, linefmt='y-', markerfmt='yo', basefmt=' ', label="Target",
    m, _, _ = ax.stem(V_guess, linefmt='r', markerfmt='rx', basefmt=' ', label="Guess",

    ax.set_title("Guess V(s)\n" + reward_guesser_title_hint(r1, r2))
  except NotImplementedError:
    print("Please finish Exercise 1 first!")

Think! 2: Examining the TD Error

Run the cell below to plot the TD errors from our multi-reward environment. A new feature appears in this plot? What is it? Why does it happen?


Click for solution

Section 3: TD-learning with probabilistic rewards

Estimated timing to here from start of tutorial: 40 min

Think! 3: Probabilistic rewards

In this environment, we’ll return to delivering a single reward of ten units. However, it will be delivered intermittently: on 20 percent of trials, the CS will be shown but the agent will not receive the usual reward; the remaining 80% will proceed as usual.

Run the cell below to simulate. How does this compare with the previous experiment?

Earlier in the notebook, we saw that changing \(\alpha\) had little effect on learning in a deterministic environment. What happens if you set it to an large value, like 1, in this noisier scenario? Does it seem like it will ever converge?

Execute this cell to see visualization

# @markdown Execute this cell to see visualization
np.random.set_state(rng_state) # Resynchronize everyone's notebooks
n_trials = 20000
  env = ProbabilisticCC(n_steps=40, reward_magnitude=10, reward_time=10,
  V_stochastic, TDE_stochastic = td_learner(env, n_trials*2, alpha=1)
  learning_summary_plot(V_stochastic, TDE_stochastic)
except NotImplementedError:
  print("Please finish Exercise 1 first")
Please finish Exercise 1 first

Click for solution


Estimated timing of tutorial: 50 min

In this notebook, we have developed a simple TD Learner and examined how its state representations and reward prediction errors evolve during training. By manipualting its environment and parameters (\(\alpha\), \(\gamma\)), you developed an intuition for how it behaves.

This simple model closely resembles the behavior of subjects undergoing classical conditioning tasks and the dopamine neurons that may underlie that behavior. You may have implemented TD-reset or used the model to recreate a common experimental error. The update rule used here has been extensively studied for more than 70 years as a possible explanation for artificial and biological learning.

However, you may have noticed that something is missing from this notebook. We carefully calculated the value of each state, but did not use it to actually do anything. Using values to plan Actions is coming up next!


Bonus Think! 1: Removing the CS

In Coding Exercise 1, you (should have) included a term that depends on the conditioned stimulus. Remove it and see what happens. Do you understand why? This phenomena often fools people attempting to train animals–beware!

Click for solution