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Diagnosing Learning Issues in Policy Gradients
A reinforcement learning agent is being trained using a simple policy gradient method to navigate a long maze. A reward of +100 is given only upon reaching the exit, and a reward of 0 is given for all other steps. The agent's performance is not improving. The current implementation updates the policy parameters at the end of each episode by weighting the gradient of the log-policy for every action taken in the episode by the total cumulative reward of that episode. Based on the principles of the Policy Gradient Theorem, which states that the policy gradient is weighted by the action-value function Q(s,a), explain why using the total episode reward as a proxy for Q(s,a) is likely causing poor learning performance in this specific scenario.
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Equivalence of Surrogate and On-Policy Gradients at the Reference Point
In a reinforcement learning scenario, an agent is in a particular state and has two possible actions, Action A and Action B. The agent's current parameterized policy assigns a non-zero probability to both actions. After sampling several trajectories, the agent estimates that the expected cumulative reward for taking Action A from this state is +10, while the expected cumulative reward for taking Action B from this state is -5. Based on the fundamental principle of updating a policy to maximize expected returns, how will the gradient update affect the probabilities of these actions?
Diagnosing Learning Issues in Policy Gradients
An agent's learning process involves updating its decision-making parameters (θ) based on experience. The update rule is proportional to the expression: Σ_s ρ(s) Σ_a ∇_θ π(s,a) Q(s,a). Match each mathematical component from this expression to its conceptual role in guiding the learning update.