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Action in Reinforcement Learning
An action is the set of all possible moves the agent can make. Agents choose from a list of possible actions. In video games, for example, the list might include: running right or left, jumping high or low, crouching or standing still.
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Foundations of Large Language Models Course
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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
Actor-Critic Methods
Deep Reinforcement Learning with Double Q-learning
Q-learning
Combining Off and On-Policy Training in Model-Based Reinforcement Learning
MuZero
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
Action in the Context of LLMs
A simple robotic arm is being trained to sort objects on a conveyor belt. The arm can perform only three distinct movements from its resting position: it can pick up an object, it can place an object in a bin, or it can do nothing and wait. In this learning scenario, what does the set {pick up, place, wait} represent?
Evaluating an Agent's Action Set
Smart Thermostat Agent Actions