Reinforcement Learning and MDPs

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Questions and Answers

What is the primary goal of reinforcement learning?

To create a static algorithm for predictions

To learn a policy that maximizes expected rewards (correct)

To minimize the cost of actions over time

To evolve states randomly according to fixed probabilities

In Markov Decision Processes, what influences the transition probabilities?

Fixed constants defined at the start

The previous states in sequence

The chosen action by the agent (correct)

The current state only

What is a unique feature of reinforcement learning compared to supervised and unsupervised learning?

Random selection of training examples

The use of labeled data for training

Immediate feedback from the environment

A feedback loop that drives learning (correct)

Which algorithm is used to solve Markov Decision Processes by calculating optimal state values?

Value Iteration Algorithm Signup and view all the answers

What does the Q-value represent in the context of Q-Learning?

Expected rewards for state-action pairs Signup and view all the answers

What is the first step in the Value Iteration Algorithm?

Initializing the value of all states to 0 Signup and view all the answers

Which of the following best describes a Markov chain?

A fixed number of states that evolves randomly with no memory Signup and view all the answers

What type of learning algorithm is Q-Learning classified as?

Model-free reinforcement learning Signup and view all the answers

How many iterations does the Q-Learning algorithm typically take to converge?

8,000 iterations Signup and view all the answers

What does the error in Q-value update represent?

The difference between the target and current Q-value Signup and view all the answers

What approach does the ε-greedy policy use?

Balancing exploration and exploitation Signup and view all the answers

What is the primary purpose of using replay memory in Deep Q Learning?

To store experiences and sample them randomly for training Signup and view all the answers

In policy-based methods, what is directly learned instead of a value function?

An optimal policy function Signup and view all the answers

What optimization method is used within the Policy Gradient framework?

Gradient ascent Signup and view all the answers

Which of the following statements about Temporal Difference learning is correct?

It operates similarly to Q-Learning and utilizes state values. Signup and view all the answers

Why is calculating the true gradient of the objective function in Policy Gradient considered computationally expensive?

It involves computing probabilities of all possible trajectories. Signup and view all the answers

Study Notes

Reinforcement Learning (RL)

RL used for stock trading strategies by modeling trading as a Markov Decision Process (MDP)
Intelligent agent interacts with environment, observes states, takes actions, and receives rewards
Agent uses policy to decide actions
Goal: learn policy maximizing expected cumulative rewards
RL unique feedback loop; not found in supervised or unsupervised learning

Markov Decision Processes (MDPs) and Dynamic Programming

Markov chains: fixed number of states, random transitions
Transition probabilities depend only on current and next state, not past states (memoryless)
MDPs: agent can choose actions, transition probabilities depend on actions, state transitions yield rewards
Goal: find policy maximizing cumulative rewards
Bellman Optimality Equation: calculates optimal value (maximum expected reward)
Value of a state: immediate reward + discounted value of future states weighted by probabilities
Dynamic Programming: complex problems into smaller, simpler subproblems

Value Iteration Algorithm

Calculates optimal value of all states using Bellman Optimality Equation
Initializes state values to 0
Iteratively updates state values until convergence
Once optimal values known, optimal policy derived by maximizing rewards
Example: Q-value iteration (works with state-action values Q(s,a) instead of state values V(s))
Requires knowing transition probabilities (T) or rewards (R)

Q-Learning

Model-free RL algorithm; estimates Q-values without knowing T or R
Learns from experience, observing transitions (s → s′ with reward r)
Updates Q-value estimates for (s, a) based on error (difference from target and current Q-value)
ε-greedy policy: balances exploration (random actions) and exploitation (highest Q-value action)
Temporal Difference (TD) learning similar but with state values instead of state-action values
Q-Value iteration converges faster (less than 20 iterations) than Q-Learning (8,000 iterations)

Deep Q-Learning

Large, complex environments
Scalable neural network approximates Q-values via a Deep Neural Network (DNN)
Experiences stored in replay memory
Reduces correlations and stabilizes learning by randomly sampling experiences
Faster training and more stable learning

Policy Gradient

Policy-based method: learns optimal policy directly, not the value function
Parameterizes policy function (e.g., neural network)
Policy outputs probability distribution over actions for a given state
Optimizes policy to maximize cumulative reward by defining and maximizing objective function to generate the best probabilities to be used as actions over time
Policy gradient is an optimisation problem, gradient ascent is used; find the values of parameters that maximise objective function
Can't calculate true gradient due to computational cost of all possible trajectories
Policy Gradient Theorem reformulates objective function for gradients calculation

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Description

Explore the concepts of Reinforcement Learning (RL) and Markov Decision Processes (MDPs) through this quiz. Learn how intelligent agents make decisions to maximize their rewards while understanding the unique feedback mechanisms involved. Test your knowledge on dynamic programming and the Bellman Optimality Equation as you delve into advanced trading strategies.