Research map / Reinforcement learning / MDPs and planning
Provably Efficient Exploration in Policy Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add optimism bonus (UCB) to the Q-function in policy evaluation | OPPO policy optimization · Episodic linear MDP, unknown transitions, adversarial rewards | regret | Helped |
| KL regularization in policy improvement (conservative, stepsize alpha finite) vs fully greedy update | OPPO policy improvement · Adversarial reward linear MDP | regret | Helped |
| Fully greedy policy improvement instead of KL-regularized update (value-based style) | OPPO in stationary setting · Stationary linear MDP | sample efficiency | No clear effect |
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