- Reinforcement learning (RL) is a branch of machine learning where the learning occurs via interacting with an environment.
- RL works by train and error method, unlike other ML paradigms.
- Agents are the software programs that make intelligent decisions and they are basically learners in RL.
- Policy function specifies what action to take in each state and value function specifies the value of each state.
- In model-based agent use the previous experience whereas in model-free learning there won't be any previous experience.
- Deterministic, stochastic, fully observable, partially observable, discrete continuous, episodic and non-episodic.
- OpenAI Universe provides rich environments for training RL agents.
- Refer section Applications of RL.
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Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
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Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.
Read more about Sudharsan Ravichandiran