feat: RMA-style history-conditioned policy for sim2real adaptation

Added a temporal observation history buffer and 1D-CNN encoder so the
policy can implicitly infer environment parameters (mass, friction,
gear ratios, etc.) from recent (obs, action) dynamics.

Architecture:
  history window [(obs₀,a₀), ..., (obs_{H-1},a_{H-1})]
      → 1D-CNN HistoryEncoder → embedding (32-dim)
      → concat [current_obs, embedding] → MLP → action

Components:
- BaseRunner: history ring buffer, _push_history/_reset_history,
  augmented obs space (6 + H×7 = 76 with H=10)
- HistoryEncoder (src/models/mlp.py): 2-layer temporal Conv1d + GAP
- SharedMLP: optional history_length/raw_obs_dim/embedding_dim params;
  splits augmented obs, encodes history, feeds [obs, emb] to MLP
- TrainerConfig: history_length, embedding_dim fields
- All runner configs: history_length=10 by default
- Tests: encoder shape, model with/without history, config defaults

configs/training/ppo.yaml

@@ -18,6 +18,10 @@ max_log_std: 2.0
 
 record_video_every: 10000
 
+# RMA-style history encoder
+history_length: 10       # temporal window (must match runner)
+embedding_dim: 32        # history encoder output dimension
+
 # ClearML remote execution (GPU worker)
 remote: false