Basic Policy Gradient Approach
A basic policy gradient approach to reinforcement learning involves three main steps. First, sample a number of state-action sequences (trajectories) based on a given policy. Second, evaluate each sampled sequence using a performance function that measures the expected cumulative reward. Third, update the model parameters to maximize this performance function, typically by employing optimization algorithms such as gradient descent.
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Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Optimal Policy Parameters via Maximization Formula
An engineer is training a system using a reinforcement learning approach. The system's behavior is determined by a set of adjustable parameters. The training process aims to find the parameter values that maximize a specific 'performance function,' which represents the expected cumulative reward. The engineer runs two separate training procedures, Procedure X and Procedure Y, and observes the following final outcomes:
- Procedure X: The final set of parameters results in a performance funct
Evaluating Policy Effectiveness
Identifying Optimal Policy Parameters from Training Data
Basic Policy Gradient Approach