simforge-rl

Train driving policies in SimForge on your own machine. simforge-rl gives you Gymnasium environments over the SimForge runtime, task sets on the released SimForge maps, training on SimForge dataset exports, and a PPO example. Apache-2.0. Dependencies: numpy and gymnasium; the PPO example adds torch (pip install 'simforge-rl[ppo]').

The episodes run in the SimForge runtime, a closed program you install after signing in; this package is the open client. It never imports runtime code and never substitutes another simulator: no runtime, no episode. Training runs locally; there is no cloud RL.

Quickstart (CPU only)

State observations need no GPU. With the simforge CLI installed:

simforge login                                                 # a browser approves the device code
simforge runtime install --accept-license                      # the closed runtime, under ~/.local/share/simforge/runtime
simforge maps pull richmond-field-station@v9 --accept-license  # a released map
pip install 'simforge-rl[ppo]'
python -m simforge_rl.examples.ppo --tasks richmond-field-station --runtime --num-envs 8 --total-timesteps 200000 --out run/

run/ then holds curve.csv, curve.svg, metadata.json and policy.pt. --runtime runs each vector worker's episodes in its own simforge-runtime serve process; without it the example starts simforge env serve processes through the CLI instead. Both give the same observations and rewards.

In Python:

from simforge_rl import RuntimeSessions, ScenarioSet, make_vector_env

tasks = ScenarioSet.bundled("richmond-field-station")          # 24 tasks: 15 templates at verified sites
with RuntimeSessions(tasks, "--no-sensors", workdir="work") as sessions:
    envs = make_vector_env(tasks, sessions.transports(), num_envs=8)
    obs, info = envs.reset(seed=0)
    obs, reward, terminated, truncated, info = envs.step(envs.action_space.sample())   # [target_speed_mps, target_accel_mps2] per env

Task sets on the released maps

bundled_sets() names the task sets shipped with the package: belmont-research-center, easterbrook-discovery-school, el-camino-road, richmond-field-station and yale-street, 24 to 29 tasks each. Each is a weighted set of scenario templates (car following, cut-ins, lead braking, crossing pedestrians and cyclists, unprotected turns, red-light runners, vehicles pulling out, ...), each at the sites of that map where it compiles and its authored episode runs, with every template weighted equally. A set is pinned to the OpenDRIVE digest of the map version it was verified on, so its episodes run on exactly that installed map: pull NAME@VERSION as the set's map says. evidence/task-sets/report.json records every template, site and verification episode. Site ids come from the map pack's derived road data, so a republished pack comes with rebuilt sets (scripts/build_task_sets.py); a site the installed pack does not have fails with no_site rather than moving the task elsewhere.

Every actor in the templates is in the public CARLA asset pack.

Train on a dataset export

tasks = ScenarioSet.from_export("exports/my-dataset", split="train")   # a downloaded folder
tasks = ScenarioSet.from_export("run_01JA...", split="train")          # your cloud export, pulled with `simforge datasets pull`

python -m simforge_rl.examples.ppo --export exports/my-dataset --split train --runtime --max-open 16 --eval-limit 8 --out run/

Both dataset formats work: the public SimForge dataset (simforge.public-dataset/v1) and cloud exports (simforge.dataset-export/v1, POST /api/v1/exports). Every file is checked against its recorded digest and laid out once; an episode runs on the installed map the scenario's OpenDRIVE digest names. --max-open keeps at most that many sessions open per worker (least recently used close first), so a split of thousands of scenarios trains with a bounded number of sessions.

Camera observations

observation="sensors" returns rendered camera passes keyed "<sourceId>/<pass>" (rgb uint8 HxWx4, depth float32 HxW) and nothing else: the policy sees no engine state, object ids, reward terms or events. Rendering needs a supported GPU and an installed map's 3D pack. simforge_rl/examples/rigs/alpamayo-fisheye.car.rig.json is an Alpamayo-style rig: a 120-degree f-theta (fisheye) front camera and a 30-degree front tele camera, 512x384. Its lens is a generic equidistant calibration, not one taken from a real vehicle.

simforge env serve ws --socket cam.sock --rig alpamayo-fisheye.car.rig.json --observation-profile sensors \
  --passes rgb,depth --actor-closure "$(basename ~/.local/share/simforge/runtime/share/actors/closures/*.json .json)"

env = SimForgeEnv(SocketTransport("cam.sock"), observation="sensors")
obs, info = env.reset(options={"episode_seed": 1})
frame = obs["camera_front_wide_120fov/rgb"]          # (384, 512, 4) uint8

ws is a scenario workspace, for example template_workspace(...) of a bundled task. simforge rig show alpamayo-fisheye.car.rig.json reports each camera's field of view; the installed runtime plans the f-theta lens.

Rewards

reward_fn(native_reward, info) -> float replaces the reward; info["native_reward"] keeps the runtime's. WeightedTerms({"progress": 1.0, "proximity": 2.0, "comfort": 1.0}) reweights the runtime's reward terms and EventPenalty({"collision": 10.0}) subtracts per engine event; both need a state observation mode.

The PPO result

evidence/ppo-scenario-set is a recorded PPO run on the bundled Richmond Field Station set through the distributable runtime, with its learning curve, configuration and a same-seed repeat.

The policy sees pose-free ego state and the eight nearest object rows and outputs a speed and acceleration setpoint. Each evaluation also runs the scenarios' authored ego control on the same tasks as a baseline. python -m simforge_rl.examples.ppo --help lists every flag.

More

• API reference: environments, task sets, exports, the runtime protocol client (episodes, CPU batches of up to 64 episodes, persistent worlds) and the PPO flags.
• RL methodology: what the environment simulates, how it is evaluated, and what has been measured.

Development

uv run --project clients/rl --extra dev python -m pytest -q clients/rl/tests
python3 scripts/sdk/toolkit-release.py build --ref HEAD --out "$TMPDIR/wheels"   # reproducible wheels

SIMFORGE_BIN and SIMFORGE_RL_TEST_MAP_DIR (Richmond Field Station v9, in a directory named richmond-field-station) enable the live runtime tests; SIMFORGE_RL_TEST_SENSOR_MAP and SIMFORGE_RL_TEST_ACTOR_CLOSURE add the camera test. Without them those tests are skipped and say so. scripts/build_task_sets.py rebuilds the bundled task sets from installed map packs.