Reinforcement Learning [Part 2]: The Q-learning Algorithm | HackerNoon (2024)

Q-learningis an RL algorithm to find the optimal q-value function. Itis a fundamental algorithm in RL, that lies behind the impressive achievements in this field in the last 10 years.

This is part 2 of my hands-on course on reinforcement learning, which takes you from zero to HERO 🦸‍♂️.

If you missedpart1, please read it to get the reinforcement learning jargon and basics in place.

Today we will learn about Q-learning, a classic RL algorithm born in the 90s.

And we will train an agent to drive a taxi 🚕🚕🚕!

Well, a simplified version of a taxi environment, but a taxi at the end of the day.

All the code for this lesson is inthis Github repo.Git clone it to follow along with today’s problem.

Contents

  1. The taxi driving problem 🚕
  2. Environment, actions, states, rewards
  3. Random agent baseline 🤖🍷
  4. Q-learning agent 🤖🧠
  5. Hyper-parameter tuning 🎛️
  6. Recap ✨
  7. Homework 📚
  8. What’s next? ❤️

1. The taxi driving problem 🚕

We will teach an agent to drive a taxi using Reinforcement Learning.

Driving a taxi in the real world is a very complex task to start with. Because of this, we will work in a simplified environment that captures the 3 essential things a good taxi driver does, which are:

  • pick up passengers and drop them at their desired destination.
  • drive safely, meaning no crashes.
  • drive them in the shortest time possible.

We will use an environment from OpenAI Gym, called theTaxi-v3environment.

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There are four designated locations in the grid world indicated by R(ed), G(reen), Y(ellow), and B(lue).

When the episode starts, the taxi starts off at a random square and the passenger is at a random location (R, G, Y or B).

The taxi drives to the passenger’s location, picks up the passenger, drives to the passenger’s destination (another one of the four specified locations), and then drops off the passenger. While doing so, our taxi driver needs to drive carefully to avoid hitting any wall, marked as|. Once the passenger is dropped off, the episode ends.

Before we get there, let’s understand well what are the actions, states, and rewards for this environment.

2. Environment, actions, states, rewards

👉🏽 notebooks/00_environment.ipynb

Let’s first load the environment:

import gymenv = gym.make("Taxi-v3").env

What are the actions the agent can choose from at each step?

  • 0 drive down
  • 1 drive up
  • 2 drive right
  • 3 drive left
  • 4 pick up a passenger
  • 5 drop off a passenger
print("Action Space {}".format(env.action_space))

And the states?

  • 25 possible taxi positions, because the world is a 5x5 grid.

  • 5 possible locations of the passenger, which are R, G, Y, B, plus the case when the passenger is in the taxi.

  • 4 destination locations

Which gives us 25 x 5 x 4 = 500 states

print("State Space {}".format(env.observation_space))

What about rewards?

  • -1 default per-step reward. Why -1, and not simply 0? Because we want to encourage the agent to spend the shortest time, by penalizing each extra step. This is what you expect from a taxi driver, don’t you?

  • +20 reward for delivering the passenger to the correct destination.

  • -10 reward for executing a pickup or dropoff at the wrong location.

You can read the rewards and the environment transitions (state, action ) → next_state from env.P.

# env.P is double dictionary.# - The 1st key represents the state, from 0 to 499# - The 2nd key represens the action taken by the agent,# from 0 to 5# examplestate = 123action = 0 # move south# env.P[state][action][0] is a list with 4 elements# (probability, next_state, reward, done)# # - probability# It is always 1 in this environment, which means# there are no external/random factors that determine the# next_state# apart from the agent's action a.## - next_state: 223 in this case# # - reward: -1 in this case## - done: boolean (True/False) indicates wheter the# episode has ended (i.e. the driver has dropped the# passenger at the correct destination)print('env.P[state][action][0]: ', env.P[state][action][0])

By the way, you can render the environment under each state to double-check this env.P vectors make sense:

From state=123

# Need to call reset() at least once before render() will workenv.reset()env.s = 123env.render(mode='human')

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the agent moves south action=0 to get to state=223

env.s = 223env.render(mode='human')

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And the reward is -1, as neither the episode ended, nor the driver incorrectly picked or dropped.

3. Random agent baseline 🤖🍷

👉🏽 notebooks/01_random_agent_baseline.ipynb

Before you start implementing any complex algorithm, you should always build a baseline model.

This advice applies not only to Reinforcement Learning problems but Machine Learning problems in general.

It is very tempting to jump straight into the complex/fancy algorithms, but unless you are really experienced, you will fail terribly.

Let’s use a random agent 🤖🍷 as a baseline model.

class RandomAgent: """ This taxi driver selects actions randomly. You better not get into this taxi! """ def __init__(self, env): self.env = env def get_action(self, state) -> int: """ We have `state` as an input to keep a consistent API for all our agents, but it is not used. i.e. The agent does not consider the state of the environment when deciding what to do next. This is why we call it "random". """ return self.env.action_space.sample()agent = RandomAgent(env)

We can see how this agent performs for a given initialstate=123

# set initial state of the environmentenv.reset()state = 123env.s = stateepochs = 0penalties = 0 # wrong pick up or dropp offreward = 0# store frames to latter plot themframes = []done = Falsewhile not done: action = agent.get_action(state) state, reward, done, info = env.step(action) if reward == -10: penalties += 1 frames.append({ 'frame': env.render(mode='ansi'), 'state': state, 'action': action, 'reward': reward } ) epochs += 1 print("Timesteps taken: {}".format(epochs))print("Penalties incurred: {}".format(penalties))

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1,420 steps is a lot! 😵

You will get different numbers when you run this code on your laptop, because of the randomness in this agent. But still, the results will be consistently bad.

To get a more representative measure of performance, we can repeat the same evaluation loopn=100times starting each time at a random state.

from tqdm import tqdmn_episodes = 100# For plotting metricstimesteps_per_episode = []penalties_per_episode = []for i in tqdm(range(0, n_episodes)): # reset environment to a random state state = env.reset() epochs, penalties, reward, = 0, 0, 0 done = False while not done: action = agent.get_action(state) next_state, reward, done, info = env.step(action) if reward == -10: penalties += 1 state = next_state epochs += 1 timesteps_per_episode.append(epochs) penalties_per_episode.append(penalties)

If you plottimesteps_per_episodeandpenalties_per_episodeyou can observe that none of them decreases as the agent completes more episodes. In other words, the agent is NOT LEARNING anything.

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If you want summary statistics of performance you can take averages:

print(f'Avg steps to complete ride: {np.array(timesteps_per_episode).mean()}')print(f'Avg penalties to complete ride: {np.array(penalties_per_episode).mean()}')

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Implementing agents that learn is the goal of Reinforcement Learning, and of this course too.

Let’s implement our first “intelligent” agent using Q-learning, one of the earliest and most used RL algorithms that exist.

4. Q-learning agent 🤖🧠

👉🏽 notebooks/02_q_agent.ipynb

Q-learning(byChris Walkins🧠 andPeter Dayan🧠) is an algorithm to find the optimal q-value function.

As we said inpart 1, the q-value functionQ(s, a)associated with a policy**π**is the total reward the agent expects to get when at statesthe agent takes actionaand follows policyπthereafter.

The optimal q-value function**Q*(s, a)**is the q-value function associated with the optimal policyπ*.

If you knowQ*(s, a)you can infer π*: i.e. you pick as the next action the one that maximizes Q*(s, a) for the current state s.

Q-learning is an iterative algorithm to compute better and better approximations to the optimal

q-value functionQ*(s, a), starting from an arbitrary initial guessQ⁰(s, a)

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In a tabular environment likeTaxi-v3with a finite number of states and actions, a q-function is essentially a matrix. It has as many rows as states and columns as actions, i.e. 500 x 6.

Ok,but how exactly do you compute the next approximation Q¹(s, a) from Q⁰(s, a)?

This is the key formula in Q-learning:

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As our q-agent navigates the environment and observes the next states’and rewardr, you update your q-value matrix with this formula.

What is the learning rate𝛼in this formula?

Thelearning rate(as usual in machine learning) is a small number that controls how large are the updates to the q-function. You need to tune it, as too large of a value will cause unstable training, and too small might not be enough to escape local minima.

And this discount factor𝛾?

Thediscount factoris a (hyper) parameter between 0 and 1 that determines how much our agent cares about rewards in the distant future relative to those in the immediate future.

  • When 𝛾=0, the agent only cares about maximizing immediate reward. As it happens in life, maximizing immediate reward is not the best recipe for optimal long-term outcomes. This happens in RL agents too.

  • When 𝛾=1, the agent evaluates each of its actions based on the sum total of all of its future rewards. In this case the agent weights equally immediate rewards and future rewards.

The discount factor is typically an intermediate value, e.g. 0.6.

To sum up…

if you

  • train long enough
  • with a decent learning rate and discount factor
  • and the agent explores enough the state space
  • and you update the q-value matrix with the Q-learning formula

your initial approximation will eventually converge to optimal q-matrix.

Voila!

Let’s implement a Python class for a Q-agent then.

import numpy as npclass QAgent: def __init__(self, env, alpha, gamma): self.env = env # table with q-values: n_states * n_actions self.q_table = np.zeros([env.observation_space.n, env.action_space.n]) # hyper-parameters self.alpha = alpha # learning rate self.gamma = gamma # discount factor def get_action(self, state): """""" return np.argmax(self.q_table[state]) def update_parameters(self, state, action, reward, next_state): """""" # Q-learning formula old_value = self.q_table[state, action] next_max = np.max(self.q_table[next_state]) new_value = \ old_value + \ self.alpha * (reward + self.gamma * next_max - old_value) # update the q_table self.q_table[state, action] = new_value

Its API is the same as for theRandomAgentabove, but with an extra methodupdate_parameters(). This method takes the transition vector(state, action, reward, next_state)and updates the q-value matrix approximationself.q_tableusing the Q-learning formula from above.

Now, we need to plug this agent into a training loop and call itsupdate_parameters()method every time the agent collects a new experience.

Also, remember we need to guarantee the agent explores enough the state space. Remember the exploration-exploitation parameter we talked about inpart 1? This is when theepsilonparameter enters into the game.

Let’s train the agent forn_episodes = 10,000and useepsilon = 10%

import randomfrom tqdm import tqdm# exploration vs exploitation probepsilon = 0.1n_episodes = 10000# For plotting metricstimesteps_per_episode = []penalties_per_episode = []for i in tqdm(range(0, n_episodes)): state = env.reset() epochs, penalties, reward, = 0, 0, 0 done = False while not done: if random.uniform(0, 1) < epsilon: # Explore action space action = env.action_space.sample() else: # Exploit learned values action = agent.get_action(state) next_state, reward, done, info = env.step(action) agent.update_parameters(state, action, reward, next_state) if reward == -10: penalties += 1 state = next_state epochs += 1 timesteps_per_episode.append(epochs) penalties_per_episode.append(penalties)

And plottimesteps_per_episodeandpenalties_per_episode

import pandas as pdimport matplotlib.pyplot as pltfig, ax = plt.subplots(figsize = (12, 4))ax.set_title("Timesteps to complete ride") pd.Series(timesteps_per_episode).plot(kind='line')plt.show()fig, ax = plt.subplots(figsize = (12, 4))ax.set_title("Penalties per ride") pd.Series(penalties_per_episode).plot(kind='line')plt.show()

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Nice! These graphs look much much better than for theRandomAgent. Both metrics decrease with training, which means our agent is learning 🎉🎉🎉.

We can actually see how the agent drives starting from the samestate = 123as we used for theRandomAgent.

# set initial state of the environmentstate = 123env.s = stateepochs = 0penalties = 0reward = 0# store frames to latter plot themframes = []done = Falsewhile not done: action = agent.get_action(state) next_state, reward, done, info = env.step(action) agent.update_parameters(state, action, reward, next_state) if reward == -10: penalties += 1 frames.append({ 'frame': env.render(mode='ansi'), 'state': state, 'action': action, 'reward': reward } ) state = next_state epochs += 1 print("Timesteps taken: {}".format(epochs))print("Penalties incurred: {}".format(penalties))

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If you want to compare hard numbers you can evaluate the performance of the q-agent on, let’s say, 100 random episodes and compute the average number of timestamps and penalties incurred.

A little bit about epsilon-greedy policies

When you evaluate the agent, it is still good practice to use a positiveepsilonvalue, and notepsilon = 0.

Why so? Isn’t our agent fully trained? Why do we need to keep this source of randomness when we choose the next action?

The reason is to prevent overfitting. Even for such a small state, action space inTaxi-v3(i.e. 500 x 6) it is likely that during training our agent has not visited enough certain states.

Hence, its performance in these states might not be 100% optimal, causing the agent to get “caught” in an almost infinite loop of suboptimal actions.

If epsilon is a small positive number (e.g. 5%) we can help the agent escape these infinite loops of suboptimal actions.

By using a small epsilon at evaluation we are adopting a so-calledepsilon-greedy strategy.

Let’s evaluate our trained agent onn_episodes = 100usingepsilon = 0.05.Observe how the loop looks almost exactly as the train loop above, but without the call toupdate_parameters()

import randomfrom tqdm import tqdm# exploration vs exploitation probepsilon = 0.05n_episodes = 100# For plotting metricstimesteps_per_episode = []penalties_per_episode = []for i in tqdm(range(0, n_episodes)): state = env.reset() epochs, penalties, reward, = 0, 0, 0 done = False while not done: if random.uniform(0, 1) < epsilon: # Explore action space action = env.action_space.sample() else: # Exploit learned values action = agent.get_action(state) next_state, reward, done, info = env.step(action) agent.update_parameters(state, action, reward, next_state) if reward == -10: penalties += 1 state = next_state epochs += 1 timesteps_per_episode.append(epochs) penalties_per_episode.append(penalties)
print(f'Avg steps to complete ride: {np.array(timesteps_per_episode).mean()}')print(f'Avg penalties to complete ride: {np.array(penalties_per_episode).mean()}')

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These numbers look much much better than for theRandomAgent.

We can say our agent has learned to drive the taxi!

Q-learning gives us a method to compute optimal q-values. But,what about the hyper-parametersalpha,gammaandepsilon?

I chose them for you, rather arbitrarily. But in practice, you will need to tune them for your RL problems.

Let’s explore their impact on learning to get a better intuition of what is going on.

👉🏽 notebooks/03_q_agent_hyperparameters_analysis.ipynb

Let’s train our q-agent using different values foralpha(learning rate) andgamma(discount factor). As forepsilonwe keep it at 10%.

In order to keep the code clean, I encapsulated the q-agent definition insidesrc/q_agent.pyand the training loop inside thetrain()function insrc/loops.py

# No need to copy paste the same QAgent# definition in every notebook, don't you think?from src.q_agent import QAgent# hyper-parameters# RL problems are full of these hyper-parameters.# For the moment, trust me when I set these values.# We will later play with these and see how they impact learning.alphas = [0.01, 0.1, 1]gammas = [0.1, 0.6, 0.9]
import pandas as pdfrom src.loops import train# exploration vs exploitation prob# let's start with a constant probability of 10%.epsilon = 0.1n_episodes = 1000results = pd.DataFrame()for alpha in alphas: for gamma in gammas: print(f'alpha: {alpha}, gamma: {gamma}') agent = QAgent(env, alpha, gamma) _, timesteps, penalties = train(agent, env, n_episodes, epsilon) # collect timesteps and penalties for this pair # of hyper-parameters (alpha, gamma) results_ = pd.DataFrame() results_['timesteps'] = timesteps results_['penalties'] = penalties results_['alpha'] = alpha results_['gamma'] = gamma results = pd.concat([results, results_])# index -> episoderesults = results.reset_index().rename( columns={'index': 'episode'})# add column with the 2 hyper-parametersresults['hyperparameters'] = [ f'alpha={a}, gamma={g}' for (a, g) in zip(results['alpha'], results['gamma'])]

Let us plot thetimestepsper episode for each combination of hyper-parameters.

import seaborn as snsimport matplotlib.pyplot as pltfig = plt.gcf()fig.set_size_inches(12, 8)sns.lineplot('episode', 'timesteps', hue='hyperparameters', data=results)

The graph looks artsy-fartsy, but a bit too noisy 😵.

Something you can observe though is that whenalpha = 0.01the learning is slower.alpha(learning rate) controls how much we update the q-values in each iteration. Too small of a value implies slower learning.

Let’s discardalpha = 0.01and do 10 runs of training for each combination of hyper-parameters. We average thetimestepsfor each episode number, from 1 to 1000, using these 10 runs.

I created the functiontrain_many_runs()insrc/loops.pyto keep the notebook code cleaner:

from src.loops import train_many_runsalphas = [0.1, 1]gammas = [0.1, 0.6, 0.9]epsilon = 0.1n_episodes = 1000n_runs = 10results = pd.DataFrame()for alpha in alphas: for gamma in gammas: print(f'alpha: {alpha}, gamma: {gamma}') agent = QAgent(env, alpha, gamma) timesteps, penalties = train_many_runs(agent, env, n_episodes, epsilon, n_runs) # collect timesteps and penalties for this pair of # hyper-parameters (alpha, gamma) results_ = pd.DataFrame() results_['timesteps'] = timesteps results_['penalties'] = penalties results_['alpha'] = alpha results_['gamma'] = gamma results = pd.concat([results, results_])# index -> episoderesults = results.reset_index().rename( columns={'index': 'episode'})results['hyperparameters'] = [ f'alpha={a}, gamma={g}' for (a, g) in zip(results['alpha'], results['gamma'])]

It looks likealpha = 1.0is the value that works best, whilegammaseems to have less of an impact.

Congratulations! You have tuned your first learning rate in this course 🥳

Tunning hyper-parameters can be time-consuming and tedious. There are excellent libraries to automate the manual process we just followed, likeOptuna, but this is something we will play with later in the course. For the time being, enjoy the speed-up in training we have just found.

Wait, what happens with thisepsilon = 10%that I told you to trust me on?

Is the current 10% value the best?

Let’s check it ourselves.

We take the bestalphaandgammawe found, i.e.

  • alpha = 1.0
  • gamma = 0.9(we could have taken0.1or0.6too)

And train with differentepsilons = [0.01, 0.1, 0.9]

# best hyper-parameters so faralpha = 1.0gamma = 0.9epsilons = [0.01, 0.10, 0.9]n_runs = 10n_episodes = 200results = pd.DataFrame()for epsilon in epsilons: print(f'epsilon: {epsilon}') agent = QAgent(env, alpha, gamma) timesteps, penalties = train_many_runs(agent, env, n_episodes, epsilon, n_runs) # collect timesteps and penalties for this pair of # hyper-parameters (alpha, gamma) results_ = pd.DataFrame() results_['timesteps'] = timesteps results_['penalties'] = penalties results_['epsilon'] = epsilon results = pd.concat([results, results_])# index -> episoderesults = results.reset_index().rename(columns={'index': 'episode'})

And plot the resultingtimestepsandpenaltiescurves:

fig = plt.gcf()fig.set_size_inches(12, 8)sns.lineplot('episode', 'timesteps', hue='epsilon', data=results)plt.show()fig = plt.gcf()fig.set_size_inches(12, 8)sns.lineplot('episode', 'penalties', hue='epsilon', data=results)

As you can see, bothepsilon = 0.01andepsilon = 0.1seem to work equally well, as they strike the right balance between exploration and exploitation.

On the other side,epsilon = 0.9is too large of a value, causing “too much” randomness during training, and preventing our q-matrix to converge to the optimal one. Observe how the performance plateaus at around250 timestepsper episode.

In general, the best strategy to choose theepsilonhyper-parameter isprogressive epsilon-decay. That is, at the beginning of training, when the agent is very uncertain about its q-value estimation, it is best to visit as many states as possible, and for that, a largeepsilonis great (e.g. 50%)

As training progresses, and the agent refines its q-value estimation, it is no longer optimal to explore that much. Instead, by decreasingepsilonthe agent can learn to perfect and fine-tune the q-values, to make them converge faster to the optimal ones. Too large of anepsiloncan cause convergence issues as we see forepsilon = 0.9.

We will be tunning epsilons along the course, so I will not insist too much for the moment. Again, enjoy what we have done today. It is pretty remarkable.

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Congratulations on (probably) solving your first Reinforcement Learning problem.

These are the key learnings I want you to sleep on:

  • The difficulty of a Reinforcement Learning problem is directly related to the number of possible actions and states. Taxi-v3 is a tabular environment (i.e. finite number of states and actions), so it is an easy one.

  • Q-learning is a learning algorithm that works excellent for tabular environments.

  • No matter what RL algorithm you use, there are hyper-parameters you need to tune to make sure your agent learns the optimal strategy.

  • Tunning hyper-parameters is a time-consuming process but necessary to ensure our agents learn. We will get better at this as the course progresses.

👉🏽 notebooks/04_homework.ipynb

This is what I want you to do:

  1. Git clonethe repo to your local machine.

  2. Setupthe environment for this lesson01_taxi.

  3. Open01_taxi/otebooks/04_homework.ipynband try completing the 2 challenges.

I call them challenges (not exercises) because they are not easy. I want you to try them, get your hands dirty, and (maybe) succeed.

In the first challenge, I dare you to update thetrain()functionsrc/loops.pyto accept an episode-dependent epsilon.

In the second challenge, I want you to upgrade your Python skills and implement paralleling processing to speed up hyper-parameter experimentation.

As usual, if you get stuck and you need feedback drop me a line at [emailprotected].

I will be more than happy to help you.

In the next part, we are going to solve a new RL problem.

A harder one.

Using a new RL algorithm.

With lots of Python.

And there will be new challenges.

And fun!

See you soon!

Do you want to become a Machine Learning PRO, and access top courses on Machine Learning and Data Science?

👉🏽Please ⭐ the course GitHub repo


Have a great day 🧡❤️💙

Pau

Reinforcement Learning [Part 2]: The Q-learning Algorithm | HackerNoon (2024)

FAQs

What is Q-learning algorithm in reinforcement learning? ›

Q-learning is a machine learning approach that enables a model to iteratively learn and improve over time by taking the correct action. Q-learning is a type of reinforcement learning. With reinforcement learning, a machine learning model is trained to mimic the way animals or children learn.

What is the difference between SARSA and Q-learning? ›

Q-learning: As an off-policy method, Q-learning updates its Q-values using the maximum possible future reward, regardless of the action taken. This can lead to more aggressive exploration of the environment. SARSA: As an on-policy method, SARSA updates its Q-values based on the actions actually taken by the policy.

What is q * in OpenAI? ›

On November 22, a few days after OpenAI fired (and then re-hired) CEO Sam Altman, The Information reported that OpenAI had made a technical breakthrough that would allow it to “develop far more powerful artificial intelligence models.” Dubbed Q* (and pronounced “Q star”) the new model was “able to solve math problems ...

What is the best algorithm for reinforcement learning? ›

There are several algorithms that can be used to train reinforcement learning agents, such as Q-learning, policy gradient methods, and actor-critic methods. These algorithms differ in how they estimate the expected cumulative reward and update the agent's policy.

What is Q-learning simply explained? ›

Q-learning is a reinforcement learning algorithm that finds an optimal action-selection policy for any finite Markov decision process (MDP). It helps an agent learn to maximize the total reward over time through repeated interactions with the environment, even when the model of that environment is not known.

What are the disadvantages of Q-learning? ›

One of the main drawbacks of Q-learning is that it becomes infeasible when dealing with large state spaces, as the size of the Q-table grows exponentially with the number of states and actions. In such cases, the algorithm becomes computationally expensive and requires a lot of memory to store the Q-values.

What is the difference between Q-Star and Q-learning? ›

Q-star and deep Q-learning are both reinforcement learning algorithms, but they have some key differences. Q-star, also known as Q-Learning, is an off-policy algorithm that uses a Q-table to store and update the Q-values for each state-action pair. It does not involve the use of neural networks.

Is Q-Star AI real? ›

The Q-Star project is a large language model (LLM). LLMs are AI models that generate text. They generate text by predicting the next word, and answers to the same question can vary. However, the Q-Star project is said to be able to solve simple mathematical problems that were not part of its training corpus.

What is Q-learning OpenAI? ›

It is a model-free approach in reinforcement learning that does not rely on pre-existing knowledge of its environment. Instead, it adopts a learn-by-doing strategy, adjusting its actions based on the outcomes they produce — rewards or penalties.

What is the simplest reinforcement learning algorithm? ›

Q-learning is an off policy reinforcement learning algorithm that seeks to find the best action to take given the current state. It's considered off-policy because the q-learning function learns from actions that are outside the current policy, like taking random actions, and therefore a policy isn't needed.

Is reinforcement learning AI or ML? ›

Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.

What is Q network reinforcement learning? ›

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

What is the difference between deep Q learning and reinforcement learning? ›

Deep Q-Learning is a type of reinforcement learning algorithm that uses a deep neural network to approximate the Q-function, which is used to determine the optimal action to take in a given state.

What is the Q-value in deep reinforcement learning? ›

Deep Q Learning uses the Q-learning idea and takes it one step further. Instead of using a Q-table, we use a Neural Network that takes a state and approximates the Q-values for each action based on that state.

What is the Q * search algorithm? ›

The Q algorithm generates nodes in the search space, applying semantic and syntactic information to direct the search. The use of semantics permits paths to be terminated and fruitful paths to be explored. The paper is restricted to a description of the use of syntactic and semantic information in the Q algorithm.

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