Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. This guide provides an overview of some common reinforcement learning algorithms.
Common RL Algorithms
Here are some of the most widely used RL algorithms:
Q-Learning
- Q-Learning is a value-based RL algorithm that learns the optimal action-value function.
- Q-Learning
Deep Q-Network (DQN)
- DQN is an extension of Q-Learning that uses a deep neural network to approximate the action-value function.
- Deep Q-Network
Policy Gradient Methods
- Policy Gradient methods directly learn the policy that maps states to actions.
- Policy Gradient
SARSA
- SARSA is a state-action-reward-state-action (SARSA) algorithm that learns the optimal state-action value function.
- SARSA
Resources
For further reading on reinforcement learning algorithms, you can check out the following resources:
- Reinforcement Learning: An Introduction - A comprehensive book on reinforcement learning.
- OpenAI Gym - A platform for developing and comparing reinforcement learning algorithms.
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