Key Reinforcement Learning Concepts for Solving Complex Tasks

Table of Contents

• Goals and Challenges of Reinforcement Learning
• Greedy vs Epsilon-Greedy Algorithms
• Exploration vs Exploitation
• Discounting Future Rewards
• Temporal Difference vs Monte Carlo Learning
• Key Reinforcement Learning Concepts

Goals and Challenges of Reinforcement Learning

The goal of reinforcement learning is to maximize the expected future return when moving between states in an environment. This involves taking actions and receiving rewards, which can be positive, negative, or zero. Maximizing future rewards is challenging because greedy algorithms that select actions based solely on the maximum immediate reward often fail in reinforcement learning tasks.

Maximizing Future Rewards

To maximize future rewards, reinforcement learning algorithms must balance short-term and long-term rewards. Just maximizing immediate rewards can lead down paths that end up minimizing long-term rewards.

Handling Positive, Negative and Zero Rewards

Rewards in reinforcement learning can be positive, negative or zero. Negative rewards are common in tasks like maze traversal. Balancing positive and negative rewards over time is key.

Greedy vs Epsilon-Greedy Algorithms

Greedy algorithms that always pick the maximum immediate reward often fail in reinforcement learning. Epsilon-greedy algorithms, which choose random explore actions some percentage of the time, perform better by gathering more environment information.

The Problem with Greedy Algorithms

Greedy algorithms get stuck in suboptimal paths due to favoring maximum immediate rewards. Without enough random exploration, they fail to find better long-term reward paths.

Why Epsilon-Greedy Works Better

Epsilon-greedy algorithms balance exploration and exploitation by sometimes choosing random actions. This leads to discovering superior long-term rewards compared to getting stuck repeatedly chasing maximum immediate rewards.

Exploration vs Exploitation

Reinforcement learning involves a tradeoff between exploration to gather new information about the environment and exploitation to maximize rewards using current knowledge. The right balance is key for optimizing long-term rewards.

Balancing Exploration and Leveraging Knowledge

Algorithms start with high exploration rates to learn the environment, then shift to exploitation by leveraging accrued knowledge while still preserving some ongoing exploration.

Discounting Future Rewards

Future rewards must be discounted when calculating long-term returns. Reward 100 dollars 5 years from now is worth less than 100 dollars today due to the time value of money. Discount factors account for this in reinforcement learning.

Applying Discount Factors

Discount factors between 0.9 and 0.99 are commonly used to discount future rewards. Applying increasing powers of the discount factor ensures finite long-term returns for infinite tasks.

Temporal Difference vs Monte Carlo Learning

Temporal difference learning adjusts after each action while Monte Carlo assesses at the end of complete episodes. TD is better for tasks where failure is costly. Monte Carlo suits games with full information revealed in episodes.

Key Reinforcement Learning Concepts

Key concepts include stochastic environments and actions, Markov decision processes for modeling, dynamic programming for solving, and deep neural networks for approximating solutions.

FAQ

Q: What is the goal of reinforcement learning?
A: The goal is to maximize expected future rewards or returns.

Q: Why can't greedy algorithms be used?
A: Because rewards can be negative, greedy algorithms may get stuck in suboptimal solutions.

Q: What is epsilon-greedy?
A: An algorithm that balances greedy reward maximization with random exploration.