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Agent in Reinforcement Learning
In reinforcement learning, the agent is the component that functions as the learner or decision-maker. It interacts with an environment by perceiving its state, performing actions, and learning from the resulting feedback. For instance, an agent could be a robot navigating a path or a trading algorithm making financial decisions. In the context of Large Language Models (LLMs), the LLM itself often serves as the agent.
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Data Science
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Useful Website for Reinforcement learning
Environment in Reinforcement Learning
State in Reinforcement Learning
Agent in Reinforcement Learning
Action in Reinforcement Learning
Reward in Reinforcement Learning
Useful Book for Reinforcement Learning
Useful Tutorials about Math behind Reinforcement Learning
Math Behind Reinforcement Learning
Exploration/Exploitation trade-off
Classification of Reinforcement Learning Methods
On-policy vs Off-policy
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Deep Reinforcement Learning with Double Q-learning
Q-learning
Combining Off and On-Policy Training in Model-Based Reinforcement Learning
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Reinforcement Learning Process for LLMs
Analyzing a Learning System
A robot is being trained to navigate a maze to find a piece of cheese. Analyze this scenario by matching each element of the training process to its corresponding fundamental concept.
Agent-Environment Interaction Loop in Reinforcement Learning
A cat is learning to use a new automated feeder that dispenses food when a lever is pressed. Initially, the cat paws at the lever randomly. After several attempts, it presses the lever and food is dispensed. The cat begins to press the lever more frequently. Which of the following statements best analyzes the relationship between the core components in this learning scenario?
Learn After
A new automated vacuum cleaner is programmed to learn the most efficient path to clean a room. It uses its sensors to detect its current location, the position of furniture, and the amount of dirt on the floor. Based on this information, it chooses to move forward, turn left, or turn right. After each cleaning session, it receives a positive signal based on the total area cleaned and a negative signal for each time it bumps into an obstacle. It uses these signals to improve its cleaning path for the next session. In this learning system, what component is the 'agent'?
LLM as the Agent in RLHF
Identifying the Agent in a Game-Playing AI
Distinguishing System Components in a Learning Scenario
A self-driving car is being trained to navigate a city. Analyze the components of this system and match each component with its correct functional role in the learning process.