In machine learning, Reinforcement learning (RL) is the process by which an agent learns how to act in a given environment by acting and getting feedback in the form of rewards or penalties. For implementing reinforcement learning algorithms, Python offers a great ecosystem of libraries and tools. Here is a brief overview and illustration of RL in Python.
Types of Reinforcement Learning
Model-free RL: The agent learns directly from interactions with the environment without a model of the environment. Common algorithms include Q-learning and Policy Gradient methods.
Model-based RL: The agent builds a model of the environment (transition and reward functions) and uses this model to plan actions.
Value-Based vs. Policy-Based RL:
Value-Based: Focuses on learning the value of actions (e.g., Q-learning).
Policy-Based: Directly learns a policy, often using algorithms like REINFORCE or Actor-Critic methods.
On-Policy vs. Off-Policy RL:
On-Policy: The agent learns from actions taken by following its current policy (e.g., SARSA).
Off-Policy: The agent learns from actions that may have been taken by a different policy (e.g., Q-learning).
Moving on to the main topic, we have top Algorithm for Reinforcement Learning in Machine Learning
- Q-Learning
- Policy Gradient Method
- Proximal Policy Optimization
- Actor Critic Method To know more about it and to unlock the power of Knowledge, visit our course section to get more information from our great experts.