executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
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Produktivität · SKILL.md
vtrace_target_computation
vtrace_target_computationCompute IMPALA V-trace targets, clipped ratios, and policy-gradient advantages from off-policy trajectories.
Independent GitHub snapshot · Automated classification · Original documentation governs
Skill-Dokumentation
GitHubname: vtrace_target_computation description: Compute IMPALA V-trace targets, clipped ratios, and policy-gradient advantages from off-policy trajectories.
Use this skill when a recovery or implementation needs the core IMPALA V-trace equations. Inputs are rewards, discounts, values with bootstrap, target action probabilities, behavior action probabilities, and clipping thresholds. Outputs are ratios, clipped rho and c, V-trace targets, and advantages.
Workflow: validate trajectory lengths, compute importance ratios, clip them separately for target residuals and trace cutting, run the backward recursive target, and report diagnostics. Use this skill before actor-critic updates; do not let a training harness duplicate the formula without cross-checking it.
Validation: tests cover on-policy reduction to n-step Bellman returns and off-policy clipping. Limitations: the helper is deterministic and vector-free for auditability, not a high-throughput learner implementation.