You will be provided with a reference and some statements. Please determine whether each statement is 'supported', 'unsupported', or 'unknown' with respect to the reference. Please note:
First, assess whether the reference contains any valid content. If the reference contains no valid information, such as a 'page not found' message, then all statements should be considered 'unknown'.
If the reference is valid, for a given statement: if the facts or data it contains can be found entirely or partially within the reference, it is considered 'supported' (data accepts rounding); if all facts and data in the statement cannot be found in the reference, it is considered 'unsupported'.

You should return the result in a JSON list format, where each item in the list contains the statement's index and the judgment result, for example:
[
    {
        "idx": 1,
        "result": "supported"
    },
    {
        "idx": 2,
        "result": "unsupported"
    }
]

Below are the reference and statements:
<reference>
d197for5662m48.cloudfront.net



This site can’t be reached

Check if there is a typo in
d197for5662m48.cloudfront.net
.

DNS_PROBE_FINISHED_NXDOMAIN

Reload

Check if there is a typo in
d197for5662m48.cloudfront.net
.
</reference>

<statements>
1. Supervised ML models (trees, random forests, neural nets, AutoML) learn non‑linear mappings from predictors to returns or risk premia; deep RL models directly learn allocation policies that optimize risk‑adjusted reward under dynamic, multi‑period settings.
2. RL frameworks embed risk directly in the objective or constraints, e.g. Sharpe ratio (mean/variance), maximum drawdown, or CVaR‑based penalties; these can reduce left‑tail risk and drawdowns more effectively than basic mean–variance.
3. “Safe RL” approaches design reward functions or constraints explicitly in terms of risk measures (variance, CVaR), allowing agents to learn policies that manage tail losses as well as average returns.
4. Direct RL policies: Deep RL agents map market states to portfolio weights and learn strategies that maximize risk-adjusted returns (Sharpe, drawdown-adjusted measures, CVaR-sensitive rewards) under realistic transaction costs and dynamics.
5. Empirical comparisons show RL-based allocations can achieve higher Sharpe ratios, lower maximum drawdowns, and reduced left-tail risks than static mean-variance benchmarks.
6. Deep RL for allocation: Risk measure / handling: Risk coded into reward or constraints (Sharpe, drawdown, CVaR, variance penalties).
7. Deep RL for allocation: Main strengths: Can reduce left‑tail risk and drawdowns; adapts to dynamics; integrates realistic frictions.
8. Deep RL for allocation: Main limitations: Hard to interpret; training instability; data‑hungry; harder to align strictly with economic theory.
9. Replace pure variance with a mean-risk formulation using CVaR or downside measures, especially for tail-risk-sensitive mandates.
10. Estimate risk via a combination of historical covariances, scenario analysis, and ML-based risk models (e.g. deep volatility or drawdown predictors), but keep the final risk measure simple and well-understood (variance + CVaR constraints, or maximum drawdown limits).
11. Use RL to learn rebalancing and timing rules on top of the baseline optimizer, e.g. controlling how aggressively to move toward the optimizer’s target weights given transaction costs and regime shifts.
12. Embed risk-adjusted rewards (Sharpe, CVaR penalties, drawdown constraints) so the policy optimizes long-run risk-return trade-offs rather than myopic one-period objectives.
13. RL and risk‑aware agents reduce left‑tail risks and improve risk‑adjusted returns versus static MV benchmarks.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.