How do you test RL exploration strategies?

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What to measure (key metrics)

• Sample efficiency: steps to reach fixed return thresholds; area under the learning curve.

• Asymptotic return: final performance given a budget (env steps + wall-clock).

• State/action coverage: visitation counts, coverage ratio, and state-entropy over time.

• Novelty/curiosity signals: intrinsic-reward magnitude vs. extrinsic return (are they aligned?).

• Regret: cumulative suboptimality (esp. for bandits and tabular tasks).

• Stability/variance: mean ± 95% CI across many seeds; failure rate.

• Generalization: performance under train/test environment variations (levels, seeds, dynamics).

Where to test (benchmarks)

• Hard-exploration Atari: Montezuma’s Revenge, Pitfall!, Private Eye.

• Procedurally generated: Procgen, MiniGrid, DMLab levels with sparse rewards.

• Continuous control (sparse): DeepMind Control / MuJoCo with goal-conditioned sparse rewards.

• Bandits & tabular MDPs: sanity checks for UCB/Thompson vs. ε-greedy.

• Custom traps: gridworlds with deceptive rewards, dead-ends, and long-horizon dependencies.

Experimental protocol (fair and reproducible)

• Fix compute budgets: environment steps, model updates, wall-clock.

• Use identical architectures and training hyperparams across methods; only exploration differs.

• Run ≥10 seeds (more for high variance tasks). Report per-seed curves, not just averages.

• Include tuning budgets and specify what was tuned to avoid cherry-picking.

• Evaluate anytime performance: intermediate checkpoints, not only final scores.

Diagnostics & ablations

• Visitation heatmaps / trajectory diversity over training phases.

• Uncertainty calibration: for UCB/Thompson/disagreement—check if high-uncertainty regions truly lack data.

• Intrinsic–extrinsic reward ratio schedules; ablate the intrinsic reward on/off.

• Reset distribution sensitivity: train with narrow resets; test with broader starts.

• Stochasticity sensitivity: add sticky actions, observation noise, stochastic resets.

• Representation tests: freeze encoder vs. trainable to see if exploration relies on features.

• Deception tests: environments with “noisy TV” to detect novelty-seeking failure modes.

Baselines to include

• Greedy (no exploration), ε-greedy, optimistic initialization.

• Count-based / pseudo-counts, RND, ICM/curiosity, disagreement/ensemble.

• For planning/bandits: UCB, Thompson sampling.

• Task-specialized: goal-conditioning (HER); long-horizon: Go-Explore-style baselines where applicable.

Analysis & statistics

• Report AUC, time-to-X (e.g., 1000 return), and best-effort final score.

• Use bootstrap CIs or t-tests with Welch correction; correct for multiple comparisons if many tasks.

• Publish learning curves + seed ribbons, tables with CIs, and per-task win/loss counts.

Common pitfalls (and how to avoid)

• Reward hacking via intrinsic reward: track extrinsic return separately; clip or normalize intrinsic scales.

• Seed cherry-picking: preregister seeds or use a deterministic list.

• Unequal compute: standardize updates, replay ratios, model sizes.

• Overfitting to a single game/level: evaluate on unseen levels/seeds.

• Non-stationary exploration schedules: visualize ε/β (intrinsic weight) over time.

Nice-to-have tools

• Logging: TensorBoard/W&B for curves, histograms, videos.

• Analysis: visitation counters, k-NN state density, mutual information between states and returns.

• Repro: environment wrappers for sticky actions, action-repeat, and deterministic eval mode.

Use this as a test plan: pick tasks from each category, set budgets, run a battery of baselines and your method across many seeds, log the diagnostics above, and report AUC, coverage, and stability.

Read more :

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What is generalization testing in RL?

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