Reinforcement Learning Algorithms

Reinforcement Learning enables agents to learn optimal behavior through interaction with an environment using rewards and penalties.

Reinforcement Learning (RL) is a machine learning paradigm where an agent learns to make decisions

by interacting with an environment. The agent receives rewards or penalties based on its actions and learns to maximize cumulative reward over time through trial and error.

Unlike supervised learning, RL doesn't require labeled training data. Instead, the agent learns through exploration and exploitation, discovering optimal policies through interaction with the environment. This makes RL particularly powerful for sequential decision-making problems where the consequences of actions unfold over time.

Overview

Key Characteristics:

• Trial and Error Learning

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  Agent learns through interaction and feedback from the environment

• Delayed Rewards

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  Actions may have consequences that are only revealed later

• Exploration vs Exploitation

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  Balance between trying new actions and using known good actions

• Policy Learning

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  Learn a mapping from states to actions that maximizes reward

Common Applications:

Game PlayingRoboticsFinanceRecommendation SystemsResource Management

• chess

• go

• poker

• video games

• navigation

• manipulation

• autonomous vehicles

• trading

• portfolio management

• risk management

• content recommendation

• ad placement

• personalization

• energy management

• traffic control

• cloud computing

Key Concepts

• Agent

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  The learner or decision maker that interacts with the environment

• Environment

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  The world in which the agent operates and receives feedback

• State

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  Current situation or configuration of the environment

• Action

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  Choice made by the agent that affects the environment

• Reward

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  Immediate feedback signal indicating the quality of an action

• Policy

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  Strategy or mapping from states to actions

• Value Function

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  Expected cumulative reward from a state or state-action pair

• Q-Function

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  Expected cumulative reward for taking an action in a state

Complexity Analysis

Complexity Overview

Time: O(|S|²|A|) to O(|S||A|) Space: O(|S||A|) to O(|S|)

Complexity depends on state space size |S|, action space size |A|, and algorithm choice

Learning ApproachesValue vs Policy Methods

Model-Based vs Model-Free

Model-Based RL:

• Learn a model of the environment
• Use the model to plan optimal actions
• More sample efficient
• Requires accurate environment modeling

Model-Free RL:

• Learn directly from experience
• No explicit environment model
• More robust to model errors
• May require more samples

Learning Approaches

1. Value-Based: Learn value functions, derive policy (Q-Learning, SARSA)
2. Policy-Based: Learn policy directly (REINFORCE, Policy Gradient)
3. Actor-Critic: Combine value and policy learning
4. Model-Based: Learn environment model, plan with it

Comparison Table

Algorithm	Status	Time Complexity	Space Complexity	Difficulty	Applications
SARSA	❓ Unknown	O(	S	×	A
Actor-Critic	❓ Unknown	O(episodes × steps_per_episode × (policy_forward_pass + value_forward_pass))	O(policy_parameters + value_parameters)	Medium	Continuous Control, Discrete Control
Policy Gradient	❓ Unknown	O(episodes × steps_per_episode × policy_forward_pass)	O(policy_parameters)	Medium	Continuous Control, Stochastic Environments
Q-Learning	📋 Planned	O(	S	×	A
Proximal Policy Optimization (PPO)	❓ Unknown	O(episodes × steps_per_episode × n_epochs × (policy_forward_pass + value_forward_pass))	O(policy_parameters + value_parameters)	Medium	Continuous Control, Discrete Control
Deep Q-Network (DQN)	❓ Unknown	O(batch_size × network_forward_pass)	O(replay_buffer_size + network_parameters)	Medium	Game Playing, Robotics

Algorithms in This Family

• SARSA - An on-policy temporal difference learning algorithm that learns action-value functions by following the current policy.

• Actor-Critic - A hybrid reinforcement learning algorithm that combines policy gradient methods with value function approximation for improved learning efficiency.

• Policy Gradient - A policy-based reinforcement learning algorithm that directly optimizes the policy using gradient ascent on expected returns.

• Q-Learning - A model-free reinforcement learning algorithm that learns optimal action-value functions through temporal difference learning.

• Proximal Policy Optimization (PPO) - A state-of-the-art policy gradient algorithm that uses clipped objective to ensure stable policy updates with improved sample efficiency.

• Deep Q-Network (DQN) - A deep reinforcement learning algorithm that uses neural networks to approximate Q-functions for high-dimensional state spaces.

Implementation Status

• Complete

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  0/6 algorithms (0%)

• Planned

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  ⅙ algorithms (17%)

Related Algorithm Families

• Dynamic-Programming: Many RL algorithms are based on dynamic programming principles

• Optimization: RL can be viewed as an optimization problem for finding optimal policies

• Neural-Networks: Deep RL combines RL with neural networks for function approximation

• Control-Theory: RL extends control theory to unknown environments

References

1. Sutton, Richard S. and Barto, Andrew G. (2018). Reinforcement Learning: An Introduction. MIT Press

Tags

Optimization Algorithms that find optimal solutions to problems

Algorithms General algorithmic concepts and implementations