pushblock/simple-v0: re-record with the action-label gap fix (46.6% of rows held a placeholder 0); band U(3.25,4.75) unchanged

pushblock/medium-v0: re-record with the action-label gap fix (61.0% of rows held a placeholder 0); band U(2.1,3.2) unchanged

pushblock/expert-v0: fix action labels — the collector recorded a fabricated 0 for every step an agent spent inside ML-Agents' action-repeat gap (72.16% of rows); it now carries the last real decision forward, so the action column matches the policy the trajectories were actually driven by

pushblock/simple-v0: fixed-T -> per-episode softmax-temperature-range tier (T~U(3.25,4.75) per episode, step-reward norm 0.6025)

pushblock/medium-v0: fixed-T -> per-episode softmax-temperature-range tier (T~U(2.1,3.2) per episode, step-reward norm 0.7984)

pushblock/simple-v0: replace epsilon tier with softmax-temperature tier (T=4.15, step-reward calibrated)

pushblock/medium-v0: replace epsilon tier with softmax-temperature tier (T=2.8, step-reward calibrated)

feat(pushblock): simple tier, per-episode epsilon pool

feat(pushblock): medium tier, per-episode epsilon pool

Replace pushblock expert-v0 with release-23 trajectories

Replace PushBlock sample with 1M real rollouts

Add discrete PushBlock expert Minari dataset