Untitled Quiz

Reinforcement Learning Basics

• The discount factor (γ) is less emphasized in episodic problems with clear termination.
• "Model-free" refers to methods that don't use a model of the environment's dynamics (e.g., Q-learning).
• "Model-based" refers to methods that use a model of the environment to make decisions (e.g., value iteration).

Value-Based Methods

• Value-based methods are suited for discrete action spaces and environments where state and action spaces are not excessively large.
• Value-based methods are used for games because they often have discrete action spaces and clearly defined rules.

Gym Environments

• Two basic Gym environments are Mountain Car and Cartpole.

Biological Name of RL

• The biological name of RL is Operant Conditioning.

RL Application

• RL is applied to sequential decision-making problems.
• Defining a reward function that accurately reflects long-term objectives without unintended side effects is the main problem of assigning reward.

MDP Elements

• The five MDP elements are States (S), Actions (A), Transition probabilities (Ta), Rewards (Ra), and Discount factor (γ).
• Agent: Actions, Policy; Environment: States, Transition probabilities, Rewards, Discount factor.

Q(s, a)

• Q(s, a) is the state-action value, representing the expected cumulative reward starting from state s, taking action a, and following policy π.

Dynamic Programming

• Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems, using the principle of optimality.

Recursion

• Recursion is a method of solving problems where the solution depends on solutions to smaller instances of the same problem.

Value Iteration

• Value iteration is a dynamic programming method used to determine the value of a state.

Typical Application Areas of RL

• Two typical application areas of RL are game playing (e.g., chess, Go) and robotics (e.g., robotic control).

Action Space and Environment

• The action space of games is typically discrete, while the action space of robots is typically continuous.
• The environment of games can be either deterministic or stochastic, but many classic board games have deterministic environments.
• The environment of robots is typically stochastic due to the unpredictability of real-world conditions.

Goal of RL

• The goal of RL is to learn a policy that maximizes the cumulative reward.

Core Problem in Deep Learning

• The main challenge in deep learning is to train deep neural networks effectively to generalize well on unseen data.

Gradient Descent

• Gradient Descent is a key optimization algorithm used in deep learning to minimize the loss function by iteratively updating the network parameters in the direction of the negative gradient of the loss.

End-to-end Learning

• End-to-end Learning is a training approach where raw input data is directly mapped to the desired output through a single, integrated process, typically using deep neural networks.

Large, High-Dimensional Problems

• Large, high-dimensional problems are characterized by vast and complex state and action spaces, common in applications such as video games and real-time strategy games.

Atari Arcade Games

• Atari Games serve as a benchmark in deep reinforcement learning research, presenting a variety of tasks that are challenging for AI due to their high-dimensional state spaces and complex dynamics.

Real-Time Strategy and Video Games

• Real-Time Strategy (RTS) Games involve managing resources, strategic planning, and real-time decision-making, making them more complex than arcade games.

Deep Value-Based Agents

• Deep value-based agents use deep learning to approximate value functions, enabling them to handle large and high-dimensional state spaces.

Model-Based Learning and Planning

• Model-based learning and planning involves learning a model of the environment's dynamics and using this model for planning and decision-making.

Hands On: PlaNet Example

• PlaNet Example is a detailed example using the PlaNet algorithm, which combines probabilistic models and planning for effective learning in high-dimensional environments.

Advantage of Model-Based Methods

• Model-based methods can achieve higher sample efficiency by using a learned model to simulate and plan actions, reducing the need for extensive interactions with the real environment.

Sample Complexity of Model-Based Methods

• In high-dimensional problems, accurately learning the transition model requires a large number of samples, which can lead to increased sample complexity.

Functions of the Dynamics Model

• The dynamics model typically includes the transition function T(s, a) = s' and the reward function R(s, a).

Deep Model-Based Approaches

• Four deep model-based approaches are PlaNet, Model-Predictive Control (MPC), World Models, and Dreamer.

End-to-End Planning and Learning

• End-to-end planning and learning can jointly optimize model learning and policy learning, leading to better integration and performance.

Dreamer and PlaNet

• Two end-to-end planning and learning methods are Dreamer and PlaNet.

Model-Based Methods

• Model-based methods are used because they can achieve higher sample efficiency by learning and utilizing a model of the environment's dynamics, which allows for better planning and decision-making with fewer interactions with the real environment.

The “Model”

• The “Model” refers to a representation of the environment's dynamics, typically including a transition function that predicts future states based on current states and actions, and a reward function that predicts the rewards received.

Difference between Model-Free and Model-Based

• Model-free methods learn policies or value functions directly from experience without explicitly modeling the environment, whereas model-based methods first learn a model of the environment's dynamics and use this model for planning and decision-making.