DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Please enter at least three characters to search
Refcards Trend Reports
Events Video Library
Refcards
Trend Reports

Events

View Events Video Library

Zones

Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks

Modernize your data layer. Learn how to design cloud-native database architectures to meet the evolving demands of AI and GenAI workkloads.

Secure your stack and shape the future! Help dev teams across the globe navigate their software supply chain security challenges.

Releasing software shouldn't be stressful or risky. Learn how to leverage progressive delivery techniques to ensure safer deployments.

Avoid machine learning mistakes and boost model performance! Discover key ML patterns, anti-patterns, data strategies, and more.

Related

  • Predicting Ad Viewability With XGBoost Regressor Algorithm
  • Quantum Machine Learning for Large-Scale Data-Intensive Applications
  • The Art of Separation in Data Science: Balancing Ego and Objectivity
  • The Transformer Algorithm: A Love Story of Data and Attention

Trending

  • How to Format Articles for DZone
  • Testing SingleStore's MCP Server
  • AI's Dilemma: When to Retrain and When to Unlearn?
  • How To Replicate Oracle Data to BigQuery With Google Cloud Datastream
  1. DZone
  2. Data Engineering
  3. AI/ML
  4. Extending Q-Learning With Dyna-Q for Enhanced Decision-Making

Extending Q-Learning With Dyna-Q for Enhanced Decision-Making

Explore Dyna-Q, an advanced reinforcement learning algorithm that extends Q-Learning by combining real experiences with simulated planning.

By 
Ashok Gorantla user avatar
Ashok Gorantla
DZone Core CORE ·
Dec. 22, 23 · Tutorial
Likes (7)
Comment
Save
Tweet
Share
19.2K Views

Join the DZone community and get the full member experience.

Join For Free

Q-Learning is a crucial model-free algorithm in reinforcement learning, focusing on learning the value, or "Q-value," of actions in specific states. This method excels in environments with unpredictability, as it doesn't need a predefined model of its surroundings. It adapts to stochastic transitions and varied rewards effectively, making it versatile for scenarios where outcomes are uncertain. This flexibility allows Q-Learning to be a powerful tool in scenarios requiring adaptive decision-making without prior knowledge of the environment's dynamics.

Learning Process

Q-learning works by updating a table of Q-values for each action in each state. It uses the Bellman equation to iteratively update these values based on the observed rewards and its estimation of future rewards. The policy – the strategy of choosing actions – is derived from these Q-values.

  • Q-Value - Represents the expected future rewards that can be obtained by taking a certain action in a given state
  • Update Rule - Q-values are updated as follows:
    • Q (state, action) ← Q (state, action) + α (reward + γ max Q (next-state,a) − Q (state, action))
    • The learning rate α indicates the importance of new information and the discount factor γ indicates the importance of future rewards.  

The code provided serves as a training function for the Q-Learner. It utilizes the Bellman equation to determine the most effective transitions between states.

Python
 
def train_Q(self,s_prime,r): 			  		 			     			  	   		   	  			  	
        self.QTable[self.s,self.action] = (1-self.alpha)*self.QTable[self.s, self.action] + \
            self.alpha * (r + self.gamma * (self.QTable[s_prime, np.argmax(self.QTable[s_prime])])) 
        self.experiences.append((self.s, self.action, s_prime, r))
        self.num_experiences = self.num_experiences + 1
        self.s = s_prime
        self.action = action
        return action 	


Exploration vs. Exploitation

 A key aspect of Q-learning is balancing exploration (trying new actions to discover their rewards) and exploitation (using known information to maximize rewards). Algorithms often use strategies like ε-greedy to maintain this balance.

Start by setting a rate for random actions to balance Exploration and Exploitation. Implement a decay rate to gradually reduce the randomness as the Q-Table accumulates more data. This approach guarantees that, over time, with the accumulation of more evidence, the algorithm increasingly shifts towards exploitation.

Python
 
if rand.random() >= self.random_action_rate:
  action = np.argmax(self.QTable[s_prime,:])  #Exploit: Select Action that leads to a State with the Best Reward
else:
  action = rand.randint(0,self.num_actions - 1) #Explore: Randomly select an Action.
    
# Use a decay rate to reduce the randomness (Exploration) as the Q-Table gets more evidence
self.random_action_rate = self.random_action_rate * self.random_action_decay_rate 


Introducing Dyna-Q

Dyna-Q, an innovative extension of the traditional Q-Learning algorithm, stands at the forefront of blending real experience with simulated planning. This approach significantly enhances the learning process by integrating actual interactions and simulated experiences, enabling agents to rapidly adapt and make informed decisions in complex environments. By leveraging both direct learning from environmental feedback and insights gained through simulation, Dyna-Q offers a comprehensive and efficient strategy for navigating challenges where real-world data is scarce or expensive to obtain.

Components of Dyna-Q

  1. Q-Learning: Learned from real experience
  2. Model Learning: Learns a model of the environment
  3. Planning: Uses the model to generate simulated experiences

Model Learning

  • The model keeps track of the transitions and rewards. For each state-action pair (s, a), the model stores the next state s′ and reward r.
  • When the agent observes a transition (s, a,r,s′), it updates the model.

Planning with Simulated Experience

  • In each step, after the agent updates its Q-Value from real experience, it also updates Q-Values based on simulated experiences.
  • These experiences are generated using the learned model: for a selected state-action pair (s, a), it predicts the next state and reward, and the Q-value is updated as if this transition had been experienced.

Algorithm Dyna-Q

  1. Initialize Q-values Q(s, a) and Model (s, a) for all state-action pairs.
  2. Loop (for each episode):
    • Initialize state s.
    • Loop (for each step of the episode):
      • Choose action a from state s using derived from Q (e.g., ϵ-greedy )
      • Take action a, observe reward r, and next state s′
      • Direct Learning: Update Q-value using observed transition (s, a,r,s′)
      • Model Learning: Update model with transition (s, a,r,s′) 
      • Planning: Repeat n times:
        • Randomly select a state-action pair (s, a) previously experienced.
        • Use model to generate predicted next state s′ and reward r
        • Update Q-value using simulated transition (s, a,r,s′)
        • s← s′.
  3. End Loop This function merges a Dyna-Q planning phase into the aforementioned Q-Learner, providing the ability to designate the desired amount of simulations to run in each episode, where actions are chosen at random. This feature enhances the overall functionality and versatility of the Q-Learner.
Python
 
def train_DynaQ(self,s_prime,r): 			  		 			     			  	   		   	  			  	
        self.QTable[self.s,self.action] = (1-self.alpha)*self.QTable[self.s, self.action] + \
            self.alpha * (r + self.gamma * (self.QTable[s_prime, np.argmax(self.QTable[s_prime])])) 
        self.experiences.append((self.s, self.action, s_prime, r))
        self.num_experiences = self.num_experiences + 1
        
        # Dyna-Q Planning - Start
        if self.dyna_planning_steps > 0:  # Number of simulations to perform
            idx_array = np.random.randint(0, self.num_experiences, self.dyna)
            for exp in range(0, self.dyna): # Pick random experiences and update QTable
                idx = idx_array[exp]
                self.QTable[self.experiences[idx][0],self.experiences[idx][1]] = (1-self.alpha)*self.QTable[self.experiences[idx][0], self.experiences[idx][1]] + \
                    self.alpha * (self.experiences[idx][3] + self.gamma * (self.QTable[self.experiences[idx][2], np.argmax(self.QTable[self.experiences[idx][2],:])])) 
        # Dyna-Q Planning - End

        if rand.random() >= self.random_action_rate:
          action = np.argmax(self.QTable[s_prime,:])  #Exploit: Select Action that leads to a State with the Best Reward
        else:
          action = rand.randint(0,self.num_actions - 1) #Explore: Randomly select an Action.
          
    	# Use a decay rate to reduce the randomness (Exploration) as the Q-Table gets more evidence
        self.random_action_rate = self.random_action_rate * self.random_action_decay_rate 
        
        self.s = s_prime
        self.action = action
        return action 	


Conclusion

Dyna Q represents an advancement, in our pursuit of designing agents that can learn and adapt in intricate and uncertain surroundings. By comprehending and implementing Dyna Q, experts and enthusiasts in the realm of AI and machine learning can devise resilient solutions to a diverse range of practical issues. The purpose of this tutorial was not to introduce the concepts and algorithms but also to ignite creativity for inventive applications and future progressions, in this captivating area of research.

Machine learning Q-learning Algorithm

Opinions expressed by DZone contributors are their own.

Related

  • Predicting Ad Viewability With XGBoost Regressor Algorithm
  • Quantum Machine Learning for Large-Scale Data-Intensive Applications
  • The Art of Separation in Data Science: Balancing Ego and Objectivity
  • The Transformer Algorithm: A Love Story of Data and Attention

Partner Resources

×

Comments
Oops! Something Went Wrong

The likes didn't load as expected. Please refresh the page and try again.

ABOUT US

  • About DZone
  • Support and feedback
  • Community research
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Core Program
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 3343 Perimeter Hill Drive
  • Suite 100
  • Nashville, TN 37211
  • support@dzone.com

Let's be friends:

Likes
There are no likes...yet! 👀
Be the first to like this post!
It looks like you're not logged in.
Sign in to see who liked this post!