Training Objective as Maximization of the Performance Function
In reinforcement learning, the primary goal of the training process is to find the optimal set of policy parameters, denoted by , that maximizes the objective or performance function, . This optimization aims to enhance the policy in a way that yields the highest possible expected cumulative reward. Formally, the optimal parameters are determined by the equation:
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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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Derivation of the Policy Gradient Objective Function
Off-Policy Objective Function with Importance Sampling
An agent is operating under a policy parameterized by . This policy can result in one of two possible trajectories. Trajectory A has a total reward of 20 and a 70% probability of occurring. Trajectory B has a total reward of -10 and a 30% probability of occurring. Given that the performance of a policy is measured by the expected cumulative reward over all possible trajectories (), what is the value of the performance function $J(\
Critique of the Expected Reward Objective
Policy Performance Comparison
On-Policy Objective Function
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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