Reinforcement Learning §

Group: 4 #group-4

Relations §

• Markov Decision Processes: Reinforcement learning problems are often modeled as Markov Decision Processes.
• Deep Learning: Deep reinforcement learning combines deep neural networks with reinforcement learning algorithms.
• Bellman Equation: The Bellman equation is a fundamental equation in reinforcement learning that relates the value function to the immediate reward and the discounted future value.
• Imitation Learning: Imitation Learning aims to learn policies by imitating demonstrations from an expert.
• Value Function Approximation: Value function approximation is used to estimate the expected future reward for a given state or state-action pair.
• Transfer Learning: Transfer Learning aims to leverage knowledge from one task to improve learning on a related task.
• Multi-Agent Reinforcement Learning: Multi-Agent Reinforcement Learning studies how multiple agents can learn to cooperate or compete in shared environments.
• Partially Observable Markov Decision Processes: Partially Observable Markov Decision Processes extend Markov Decision Processes to environments with partial observability.
• Temporal Difference Learning: Temporal Difference Learning is a class of model-free reinforcement learning algorithms that learn directly from experience.
• Q-Learning: Q-Learning is a popular model-free reinforcement learning algorithm.
• Hierarchical Reinforcement Learning: Hierarchical Reinforcement Learning aims to solve complex problems by breaking them down into hierarchical sub-tasks.
• Machine Learning: Reinforcement Learning is a type of Machine Learning where an agent learns to make decisions by interacting with an environment and receiving rewards or penalties.
• Policy Gradients: Policy gradient methods are a class of reinforcement learning algorithms that directly optimize the policy function.
• Inverse Reinforcement Learning: Inverse Reinforcement Learning aims to infer the reward function from observed behavior.
• Actor-Critic Methods: Actor-Critic methods combine value function estimation with policy optimization.
• Neural Networks: Reinforcement learning can be used to train neural networks to make decisions in an environment by maximizing a reward signal.
• Multi-Armed Bandits: Multi-Armed Bandits are a simplified reinforcement learning problem used to study exploration vs exploitation tradeoffs.
• Reward Function: The reward function defines the goal or objective that the reinforcement learning agent aims to maximize.
• Monte Carlo Methods: Monte Carlo methods are a class of reinforcement learning algorithms that learn from complete episodes.
• Exploration vs Exploitation: Reinforcement learning algorithms must balance exploration of new actions with exploitation of known good actions.
• Deep Q-Networks: Deep Q-Networks combine Q-Learning with deep neural networks for function approximation.
• Dynamic Programming: Dynamic Programming is a class of algorithms used to solve reinforcement learning problems with known environments.