PortSimEnv v1: tasks, source calls, eval rollouts

README.md

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+---
+license: cc-by-sa-4.0
+pretty_name: PortSimEnv v1
+language: [en]
+task_categories: [reinforcement-learning, text-generation]
+tags: [openenv, rl-environment, simulation, logistics, scheduling, operations-research, berth-allocation, real-world-data]
+size_categories: [1K<n<10K]
+configs:
+- config_name: tasks
+  default: true
+  data_files:
+  - split: train
+    path: tasks/train.parquet
+  - split: eval
+    path: tasks/eval.parquet
+- config_name: rollouts
+  data_files:
+  - split: eval
+    path: rollouts/eval.parquet
+- config_name: calls
+  data_files:
+  - split: train
+    path: calls/train.parquet
+---
+
+# PortSimEnv v1
+
+Re-plan container-ship dockings at the Port of Barcelona. Each task is a real week (or two or three) at one container
+quay, built from the port's own 2024 records, with disruptions added: late and bunched ships, closed quay sections,
+crane breakdowns, gales under the port's wind rules, traffic diverted from the other terminal, emergencies and priority
+cargo. The agent decides when and where each ship docks and with how many cranes, and is graded once, deterministically,
+against the plan a CP-SAT solver proved optimal.
+
+| | |
+|---|---|
+| Environment (OpenEnv Space, 3D viewer, eval explorer) | [FineEnvs/PortSimEnv](https://huggingface.co/spaces/FineEnvs/PortSimEnv) |
+| Article | [Simulation RL Environments](https://huggingface.co/spaces/FineEnvs/simulation-rl-environments) |
+| Bucket (3D twin data, raw eval rollouts) | [FineEnvs/PortSimEnv](https://huggingface.co/buckets/FineEnvs/PortSimEnv) |
+| Code | [adithya-s-k/FineEnvs](https://github.com/adithya-s-k/FineEnvs) |
+| Ideas for v2, v3, post-training, data | [GitHub Discussions](https://github.com/adithya-s-k/FineEnvs/discussions/36) |
+
+## Configs
+
+| config | split | rows | what |
+|---|---|---|---|
+| `tasks` | `train` | 1,050 | training tasks |
+| `tasks` | `eval` | 50 | held-out tasks: whole week groups that never appear in train |
+| `rollouts` | `eval` | 300 | 6 models x 50 eval tasks: transcript, tool calls, final plan, grade |
+| `calls` | `train` | 1,784 | the 2024 container calls at quays 36A (BEST) and 24B (APM Terminals) the tasks are built from |
+
+**tasks**: `system_prompt` and `situation` are exactly what the environment sends (the rules, then the opening
+message, which is also what `get_situation()` returns). `task` is the full task as JSON (ships, closures, disruptions,
+rules). `optimal_plan` / `optimal_cost` are the CP-SAT reference (`proven_optimal` says whether optimality was
+proven); `naive_plan` / `naive_cost` re-plan by pushing ships later. A plan is a JSON list of
+`{"ship": id, "berth_hour": h, "section": s, "cranes": c}`.
+
+**rollouts**: one row per (model, task) from the eval run `dock-eval50`: 12 turns and 32k output tokens per turn,
+the same three tools. `messages` is the full transcript, `steps` the tool calls with each `check_plan` result,
+`grade` the final grade.
+
+## Results on the 50 eval tasks
+
+| model | mean reward | standard | busy | storm | extreme | submitted | valid | optimal |
+|---|---|---|---|---|---|---|---|---|
+| GPT-6.1 Sol | **0.888** | 1.00 | 0.90 | 0.86 | 0.82 | 50 | 50 | 26 |
+| Claude Sonnet 5.5 | **0.782** | 0.92 | 0.80 | 0.68 | 0.77 | 49 | 49 | 10 |
+| GLM-5.3-Flash | **0.470** | 0.77 | 0.50 | 0.46 | 0.24 | 36 | 25 | 9 |
+| Qwen3.8-2.4T | **0.380** | 0.58 | 0.45 | 0.33 | 0.21 | 34 | 23 | 5 |
+| GLM-5.3 | **0.313** | 0.62 | 0.34 | 0.22 | 0.15 | 17 | 16 | 10 |
+| Qwen3.8-27B | **0.211** | 0.40 | 0.27 | 0.22 | 0.00 | 16 | 13 | 3 |
+
+## Reward
+
+Deterministic, no LLM judge. A plan that breaks any rule scores at most 0.2 (0.2 x the share of ships placed cleanly).
+A valid plan's cost (hours each ship leaves after its due time x its size x its priority, plus penalties for late
+emergency dockings and moved ships) is compared with the optimum:
+`gap = (cost - optimum) / (optimum - unavoidable + 100)`, `reward = 0.2 + 0.8 * exp(-gap / 0.5)`. No submission
+scores 0.
+
+## Use
+
+```python
+import json
+from datasets import load_dataset
+
+tasks = load_dataset("FineEnvs/PortSimEnv", "tasks", split="eval")
+task = tasks[0]
+print(task["system_prompt"], task["situation"], sep="\n\n")
+```
+
+Every task can be played on the environment Space (OpenEnv; MCP tools `get_situation`, `check_plan`,
+`submit_plan`). Submitting the reference optimum scores 1.0:
+
+```python
+from openenv.core.env_server.mcp_types import CallToolAction
+from openenv.core.mcp_client import MCPToolClient
+
+env = MCPToolClient("https://fineenvs-portsimenv.hf.space").sync()
+env.reset(task_id=task["task_id"])
+step = env.step(CallToolAction(tool_name="submit_plan", arguments={"plan": json.loads(task["optimal_plan"])}))
+print(step.reward)  # 1.0
+```
+
+## Source and licence
+
+Contains data from the Port de Barcelona open data portal (https://opendata.portdebarcelona.cat/), via the 2024
+snapshot in [alberto-santini/berth-allocation-problems](https://github.com/alberto-santini/berth-allocation-problems),
+licensed CC BY-SA 4.0. This dataset is shared under the same licence. Crane fleets, wind rules and handling rates come
+from the terminals' and the port's published information; disruptions are generated. Work in progress (v1).

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rollouts/eval.parquet

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tasks/train.parquet

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