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Johnny Fernandes
2026-05-10 12:46:14 +01:00
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.gitignore
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# Stuff
#_example/
# Editor / IDE
.claude/
.venv/
# Python
__pycache__/
*.pyc
.venv/
# Optional env parity debug
# Webots controller scratch / debug
controllers/shepherd_dog/dog_behavior_log.csv
dog_debug.csv
# Webots controller scratch
controllers/shepherd_dog/dog_behavior_log.csv
# Training artefacts
training/runs/*
!training/runs/.gitkeep
# Training artefacts: ignore by default, whitelist the two working BC policies
*.zip
*.pkl
# TensorBoard
*.npz
events.out.tfevents.*
training/runs/*/checkpoints/
training/runs/*/tb/
training/runs/*/evals/
training/runs/*/best/
!training/runs/.gitkeep
!training/runs/bc_solo/policy.zip
!training/runs/bc_flock/policy.zip
# Webots launcher scratch
worlds/field_test.wbt
herding_runtime.cfg
README.md
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# Autonomous Shepherd-Dog Herding (Webots + RL)
Group G25 — *Diogo Costa, Johnny Fernandes, Nelson Neto*
A differential-drive shepherd dog that herds 1–10 sheep through a 3 m
gate into an external pen. The dog has three modes:
| Mode | Source | Notes |
|---|---|---|
| `rl` | Behavior cloning of an analytic teacher | The deliverable RL policy |
| `strombom` | Strömbom (2014) collect/drive heuristic | Canonical baseline |
| `sequential` | Single-target "pin and push" | Robust across n=1–10 |
Plus three documented experimental teachers (`hybrid`, `drive_only`,
`strombom_smooth`) — see `herding/` for details.
## Quick start
```bash
# 1. Set up the Python env (any venv with PyTorch + SB3)
pip install -r training/requirements.txt
# 2. Smoke test
python -m training.parity_test
# 3. Reproduce the BC policy from scratch (~25 min on CPU)
python -m tools.collect_demos --teacher strombom --out training/demos.npz \
--seeds-per-n 30 --subsample 3
python -m training.bc_pretrain --demos training/demos.npz \
--out training/runs/bc_flock --epochs 100 --net-arch 512,512
# 4. Evaluate
python -m training.eval --policy training/runs/bc_flock \
--max-flock 10 --max-steps 30000 --n-seeds 5
# 5. Run in Webots (any of the three modes; n is the flock size)
HERDING_POLICY_DIR=$PWD/training/runs/bc_flock tools/run_webots.sh 10 rl
tools/run_webots.sh 10 strombom
tools/run_webots.sh 10 sequential
```
## Layout
```
herding/ — single source of truth (env + Webots both import)
geometry.py — field/pen constants, robot specs
flocking_sim.py — Reynolds-style sheep dynamics
diffdrive.py — differential-drive kinematics
obs.py — 32-D order-invariant observation builder
strombom.py — canonical CoM-drive teacher
sequential.py — single-target "pin-and-push" teacher
hybrid.py — flock-then-funnel (experimental, did not scale)
drive_only.py — Strömbom drive without collect (experimental)
strombom_smooth.py — sigmoid-blended Strömbom (experimental)
controllers/
sheep/sheep.py — Webots sheep controller (uses herding.flocking_sim)
shepherd_dog/
shepherd_dog.py — Webots dog controller, mode-switched
policy_loader.py — lazy SB3 PPO loader
strombom.py — backwards-compat shim
training/
herding_env.py — Gymnasium env (used for demo collection + eval)
bc_pretrain.py — supervised BC of analytic teachers into MLP policy
collect_demos.py — wrapper, see tools/
eval.py — RL / analytic comparison harness
parity_test.py — smoke tests
train_ppo.py — PPO/RL fine-tune (experimental, BC alone preferred)
requirements.txt
configs/ppo_default.yaml
tools/
collect_demos.py — generate (obs, action) demonstrations
run_webots.sh — launch Webots with N sheep + chosen controller mode
worlds/
field.wbt — main world (3 m gate, external pen)
protos/ — Sheep / ShepherdDog robot definitions
docs/project.md — original project goals
plan.md — design notes / decision log
```
## Two cohesion regimes
Sheep cohesion strength controls which teacher works:
| Regime | `flocking_sim.py` setting | Strömbom | Sequential |
|---|---|---:|---:|
| **Tight** (current) | `w=3.0/1.0`, `dist=12` | works (flock-style) | breaks (cohesion fights single-sheep targeting) |
| Loose | `w=1.5/0.6`, `dist=8` | breaks (flock fragments at gate) | works (1-by-1 style) |
The codebase ships with the **tight** regime. To use the loose-regime
Sequential clone, edit those constants in `herding/flocking_sim.py` and
load `training/runs/bc_solo/`.
## Results
Eval at `--max-steps 30000 --n-seeds 5`, deployment difficulty (full
field spawn distribution):
| n | Strömbom | Sequential | BC-flock (RL) |
|---:|---:|---:|---:|
| 1 | 100 % | 100 % | 100 % |
| 5 | 100 % | 100 % | 80–100 % |
| 8 | 100 % | 100 % | 80 % |
| 10 | **100 %** | 80 % | **80 %** (mean_penned 8/10) |
The BC policy hits ~80 % of the analytic teacher's success rate in 100 %
neural-network inference, with no hand-coded logic.
## License
Educational project for the *Topics in Intelligent Robotics* course.
controllers/shepherd_dog/shepherd_dog.py
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"""Shepherd Dog controller (Webots).
Runs in one of two modes selected by the ``HERDING_MODE`` environment
variable:
Mode is selected by ``HERDING_MODE`` (env var, or via the
``herding_runtime.cfg`` file the launcher writes since Webots strips
env vars on some setups):
HERDING_MODE=rl → load an SB3 PPO policy from
HERDING_POLICY_DIR (default
training/runs/latest/best) and use its
(vx, vy) action each step.
HERDING_MODE=strombom → use the analytic Strömbom collect/drive
heuristic. This is the fallback if the RL
policy can't be loaded (e.g. SB3 not
installed in the Webots Python env, or no
checkpoint yet).
rl → load a BC-trained SB3 policy from HERDING_POLICY_DIR
and use its (vx, vy) action each step.
strombom → canonical Strömbom collect/drive heuristic.
sequential → single-target "pin and push" — drives the sheep
closest to the pen.
Both modes share the same low-level differential-drive controller
(``herding.diffdrive.velocity_to_wheels`` + clamped forward speed), so
switching modes does not retune the actuation layer.
All modes share the same low-level differential-drive controller
(``herding.diffdrive.velocity_to_wheels`` with cos(err)-clamped forward
speed), so switching modes does not retune actuation.
A safety supervisor enforces the "dog stays out of the pen" invariant:
if the action would push the dog past ``DOG_SOUTH_LIMIT`` it is
overridden with a north-driving correction. This is a hard guarantee
the policy cannot escape.
overridden with a north-driving correction. RL fallback: if the policy
zip can't be loaded (SB3 missing, file missing), the controller drops
to strombom mode automatically.
"""
import math
@@ -85,19 +83,21 @@ def _resolve_policy_dir() -> str:
"""Where to look for the trained policy.
Priority:
1. HERDING_POLICY_DIR env var (if set and points to a real dir)
2. training/runs/bc_pretrained/ (BC-only checkpoint)
3. training/runs/bc_ppo/best/ (PPO fine-tuned best)
4. training/runs/latest/best/ (legacy default)
1. HERDING_POLICY_DIR env var or runtime-cfg entry, if it points
to a real directory.
2. ``training/runs/bc_flock`` — flock-style BC (current default;
requires the tight-cohesion sheep regime).
3. ``training/runs/bc_solo`` — single-target BC (1-by-1 style;
only works if ``herding/flocking_sim.py`` is reverted to the
loose-cohesion regime).
"""
env_dir = (os.environ.get("HERDING_POLICY_DIR")
or _runtime_cfg.get("HERDING_POLICY_DIR"))
if env_dir and os.path.isdir(env_dir):
return env_dir
candidates = [
os.path.join(_PROJECT_ROOT, "training", "runs", "bc_pretrained"),
os.path.join(_PROJECT_ROOT, "training", "runs", "bc_ppo", "best"),
os.path.join(_PROJECT_ROOT, "training", "runs", "latest", "best"),
os.path.join(_PROJECT_ROOT, "training", "runs", "bc_flock"),
os.path.join(_PROJECT_ROOT, "training", "runs", "bc_solo"),
]
for c in candidates:
if os.path.isdir(c):
@@ -106,30 +106,22 @@ def _resolve_policy_dir() -> str:
return env_dir or candidates[0]
POLICY_DIR = _resolve_policy_dir()
_VALID_MODES = ("rl", "strombom", "sequential")
if MODE not in _VALID_MODES:
print(f"[dog] unknown HERDING_MODE={MODE!r}; defaulting to strombom.")
MODE = "strombom"
POLICY_DIR = _resolve_policy_dir()
policy_handle = None
if MODE == "rl":
print(f"[dog] HERDING_MODE={MODE} HERDING_POLICY_DIR(env)="
f"{os.environ.get('HERDING_POLICY_DIR', '<unset>')}")
print(f"[dog] resolved POLICY_DIR={POLICY_DIR} exists="
f"{os.path.isdir(POLICY_DIR)}")
if os.path.isdir(POLICY_DIR):
try:
entries = sorted(os.listdir(POLICY_DIR))
except OSError:
entries = []
print(f"[dog] dir contents: {entries}")
print(f"[dog] resolved POLICY_DIR={POLICY_DIR} exists={os.path.isdir(POLICY_DIR)}")
try:
from policy_loader import load as _load_policy
policy_handle = _load_policy(POLICY_DIR)
print(f"[dog] RL policy loaded from {POLICY_DIR}")
except Exception as exc:
print(f"[dog] RL policy load failed ({exc!r}); falling back to Strömbom.")
print(f"[dog] RL policy load failed ({exc!r}); falling back to strombom.")
MODE = "strombom"
if MODE not in ("rl", "strombom", "sequential"):
print(f"[dog] unknown HERDING_MODE={MODE!r}; defaulting to strombom.")
MODE = "strombom"
print(f"[dog] running in mode={MODE}")
herding/flocking_sim.py
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FLEE_DIST = 7.0
SEPARATION_DIST = 2.5
COHESION_DIST = 8.0
COHESION_DIST = 12.0 # was 8.0 — wider engagement so far-flung sheep are pulled in
PEN_MARGIN = 0.8
@@ -125,12 +125,13 @@ def compute_heading_speed(x, y, penned, dog_xy, peers, wander_angle, rng=None):
cy += py
cn += 1
if cn > 0:
# Cohesion needs to be comparable to flee at close range to keep
# the flock together through narrow obstacles like the 3m gate.
# Flee at 2m has magnitude ~10; cohesion at peer-distance 5m
# with w=1.5 contributes ~7.5 — same order, so the flock
# translates as a unit instead of fragmenting under pressure.
w = 1.5 if fleeing else 0.6
# Cohesion needs to dominate flee at close range so the flock
# stays glued together when squeezing through the narrow gate.
# Flee at 2 m has magnitude ~10; cohesion of w=3.0 with the
# peer-CoM 4 m away contributes ~12, so the flock prefers
# bunching to dispersing under pressure. This is what makes
# canonical Strömbom drive work in our 3 m gate.
w = 3.0 if fleeing else 1.0
fx += (cx / cn - x) * w
fy += (cy / cn - y) * w
plan.md
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# RL-Driven Shepherd Herding — Implementation Plan
This plan turns the existing Strömbom-only Webots project into a dual-mode
shepherd controller (RL primary, Strömbom fallback), with a fast Gymnasium
training environment that mirrors the Webots dynamics tightly enough for
sim-to-sim transfer. Stable-Baselines3 PPO is the learner.
---
## 1. Current state (audit)
### World geometry — `worlds/field.wbt`
- Field bounded by stone walls at **x,y ∈ [−15, +15]**. Inside-usable area is
~[−14.5, 14.5] (`X_MIN/MAX` in `flocking.py`).
- **Pen is *inside* the field**: x ∈ [10, 13], y ∈ [−15, −8], with the
opening on its **north** side at y = −8 (post-and-rail fence W/E; open N).
- South stone wall has a **gate at x ∈ [10, 13], y = −15** (split wall +
gate posts at x=10 and x=13). So sheep that get penned end up between the
fence (N side at y=−8) and the south stone wall (with the wooden gate at
y=−15 currently slightly ajar). The pen is effectively an L-shape inside
the field, not external.
- Spawns: dog at origin (0, 0), 3 sheep around (3, ±2) and (4, 0). Two more
sheep are commented out.
### Robots — protos
- **Sheep** (`protos/Sheep.proto`): differential drive, wheel radius 0.031 m,
axle half-width 0.10 m → wheel base 0.20 m. `maxVelocity = 25 rad/s` →
max linear ≈ **0.78 m/s**. Sensors: GPS, Compass, Emitter+Receiver on
channel 1. `supervisor = TRUE` (used to repaint wool pink on pen entry).
- **ShepherdDog** (`protos/ShepherdDog.proto`): differential drive, wheel
radius 0.038 m, axle half-width 0.14 m → wheel base 0.28 m.
`maxVelocity = 70 rad/s` → max linear ≈ **2.66 m/s**. Sensors: GPS,
Compass, Gyro, Accelerometer, **Lidar** (front-only, FOV 2.44 rad ≈ 140°,
180 rays, range 0.10–12 m, noise 0.005), Emitter+Receiver on channel 1,
cosmetic ear/tail motors.
### Sheep controller — `controllers/sheep/{sheep.py,flocking.py}`
- Reynolds-style boid stack: flee (quadratic ramp inside FLEE_DIST=7 m),
cohesion (within 8 m), separation (within 2.5 m), wall soft repulsion
(margin 5 m), wall hard escape (margin 1 m, gain 50), wander.
- Pen-aware: sheep below the gate line but outside the gate corridor get a
northward "deadzone" assist; on first entry into the pen rectangle,
sheep latches `penned=True`, repaints pink, and switches to in-pen
containment + jitter.
- Driver: heading-error PD on diff-drive (k=4), forward velocity scaled by
`cos(err)`, MAX_SPEED=22 (motor units, capped by proto's 25 rad/s).
- Stuck detector: if displacement < 0.05 m for 20 steps, drives toward
field origin to escape wall-pin (a known differential-drive failure mode).
### Dog controller — `controllers/shepherd_dog/{shepherd_dog.py,strombom.py}`
- Strömbom collect/drive heuristic. CoM-radius gating
`radius > F·√n` with F=2 selects collect (push furthest sheep inward) vs
drive (push CoM toward the pen entry point at (11.5, −8.0)).
- Deadzone rescue: when a sheep is below the gate line and outside the
pen's x-corridor, the dog repositions to a "behind the sheep, opposite
the pen" stand-off so the sheep's flee vector points back through the
gate. Variants 0/1 alternate lateral offset to break corner cycles.
- Stuck-rescue, EMA action smoothing, target-deadband, RESCUE_SPEED_CAP,
cooldown — all empirical fixes for diff-drive oscillation.
- Logs full per-step debug to `dog_behavior_log.csv` (currently 7 MB —
add to `.gitignore`).
### Deleted training scaffolding (per `git status`)
- `controllers/shepherd_dog_rl/{shepherd_dog_rl.py, final_model.zip, vecnorm.pkl, plot_debug.py}`
- `training/{config.json, herding_env.py, parity_test.py, requirements.txt, train.py, train_at.py, viz.py, runs/.gitkeep}`
A previous attempt existed; we'll redesign rather than resurrect, keeping
only the lessons (parity-tested env, VecNormalize wrapper, eval cadence).
---
## 2. Design decisions
### 2.1 Pen location — keep inside-field with N gate
The user offered moving the pen *external* (through a wall hole). Tradeoffs:
| Option | Pros | Cons |
|---|---|---|
| **(A) Keep inside-field** (current) | World already built; Strömbom logic already tuned; gate corridor is short | Dog must navigate around three pen walls; adds geometric clutter |
| (B) External pen via wall hole | Cleaner field — dog only sees sheep + outer walls; pen as goal region beyond a 3 m hole at y=−15 | Requires editing `field.wbt` (split south wall, add external pen walls beyond y<−15); existing rescue/deadzone logic must be retuned; outside-field flocking constants don't currently apply |
**Recommendation: keep (A)** for parity with the working Strömbom controller,
but add a **simplification**: widen the pen entrance from 3 m (x ∈ [10, 13])
to 4 m (x ∈ [9.5, 13.5]) and raise the entrance line from y=−8 to y=−7.5
to give the dog more turning room. Optional later: gate B as a curriculum
extension (Section 7).
### 2.2 Where to train
PPO on Webots directly is too slow (real-time stepping, single env, slow
reset). The previous training scaffolding used a Python 2D sim — that is
the right approach. Constraints for sim-to-sim transfer:
1. **Use the exact same flocking math**: import `controllers/sheep/flocking.py`
from the env, do not reimplement.
2. **Use the same world constants**: import `controllers/shepherd_dog/strombom.py`
for pen geometry and Strömbom baseline.
3. **Model differential drive faithfully**: match wheel-radius, base, and
max wheel-velocity from the proto files. Heading update from
`(ω_R − ω_L)·r / b`, position from `(ω_R + ω_L)·r / 2`.
4. **Match Webots step**: `basicTimeStep = 16 ms`. The sheep controller runs
at every basic step; the env will use the same `dt = 0.016 s`.
5. **Lidar deferred**: dog policy will use a *symbolic* observation
(positions of dog + sheep, plus pen geometry) — not raw lidar — for the
first iteration. Lidar-from-pixels is a much harder learning problem
and isn't required for the herding task. (See Section 7 for an
optional later upgrade.)
### 2.3 Action space for the dog
Two viable choices:
- **(a) High-level velocity vector** `(vx, vy) ∈ [−1, 1]²`. The same
representation Strömbom emits today; the existing
`drive_action(vx, vy, ...)` function in `shepherd_dog.py` converts this
to wheel speeds. Decouples the policy from low-level diff-drive
oscillations and enables direct A/B against Strömbom.
- (b) Direct wheel speeds `(ω_L, ω_R) ∈ [−1, 1]²`. More expressive but the
policy must learn diff-drive control from scratch — which is exactly
the source of the wall-stuck and oscillation pain we're trying to
avoid.
**Recommendation: (a)** — high-level `(vx, vy)`. Reuses the well-tuned
`drive_action` controller, which already handles `cos(err)` clamping and
turn gain. RL focuses on *strategy*, not actuation.
### 2.4 Observation space for the dog
Symbolic, fixed-size, normalized to [−1, 1]:
| Field | Dim | Notes |
|---|---|---|
| Dog (x, y, cos h, sin h) | 4 | Position normalized by 15 |
| Sheep CoM (x, y) | 2 | Of *active* (not-penned) sheep |
| Sheep dispersion (radius, std-x, std-y) | 3 | Strömbom collect-vs-drive features |
| Vector dog→CoM (dx, dy, dist) | 3 | Helps the value function |
| Vector dog→pen-entry (dx, dy, dist) | 3 | |
| Vector furthest-sheep→CoM (dx, dy) | 2 | Strömbom collect target hint |
| Min sheep-to-wall distance + min dog-to-wall | 2 | Safety signal |
| Active sheep count / N_max | 1 | |
| 8-bin polar histogram of sheep around dog | 8 | Order-invariant flock shape |
Total: **28 features**. Order-invariant by construction (histogram + summary
stats), so the policy generalizes across flock sizes 1..N_max.
### 2.5 Reward
Sparse-only is too hard at flock scale; we shape conservatively.
```
r_t = w_pen · ΔN_penned # +1 per newly penned sheep
+ w_progress· (d_CoM_pen[t-1] − d_CoM_pen[t]) # closer-to-pen progress
+ w_compact· (R[t-1] − R[t]) # tighter flock progress
− w_time · 1 # constant time penalty
− w_wall · I(min_wall_dist < 1.0 m) # dog too close to wall
− w_collide· I(dog within 0.3 m of any sheep) # avoid contact
+ w_done · I(all sheep penned) # terminal bonus
```
Initial weights: `w_pen=2.0, w_progress=0.5, w_compact=0.2, w_time=0.005,
w_wall=0.01, w_collide=0.05, w_done=10.0`. Tune via 1-sheep curriculum
first — if the dog learns 1-sheep cleanly, the weights are sane.
### 2.6 Episode
- Max steps: 3000 (≈ 48 s at dt=16 ms — generous).
- Termination: all sheep penned (success), dog/sheep stuck > 600 steps with
no progress (failure), step limit (timeout).
- Reset: domain-randomized — sheep count ∈ {1..N_max}, sheep positions
uniform in field minus pen+gate corridor, dog at origin ± U(−2, 2).
### 2.7 Curriculum
| Stage | N_sheep | Duration (steps) | Pass criterion |
|---|---|---|---|
| 0 | 1 | 0.5 M | success ≥ 90 % |
| 1 | 2 | 1.0 M | success ≥ 80 % |
| 2 | 3 | 1.5 M | success ≥ 70 % |
| 3 | 1..3 mixed | 2.0 M | mean reward stable |
| 4 (optional) | 5 | 2.0 M | success ≥ 60 % |
Implemented by changing only `n_sheep` in the env reset.
---
## 3. Repository layout (new)
```
project/
├── controllers/
│ ├── sheep/ # unchanged
│ ├── shepherd_dog/ # Strömbom controller (renamed entry)
│ │ ├── shepherd_dog.py # mode-switch wrapper: RL | strombom
│ │ ├── strombom.py # unchanged (canonical Strömbom)
│ │ └── policy_loader.py # NEW: loads SB3 zip + VecNormalize
│ └── ...
├── herding/ # NEW: Python package, importable from env + controller
│ ├── __init__.py
│ ├── geometry.py # field/pen constants, in_pen(), wall helpers (single source of truth)
│ ├── flocking_sim.py # vectorised numpy port of flocking.py for fast batched sheep
│ ├── diffdrive.py # diff-drive integrator matching the proto specs
│ └── obs.py # observation builder shared by env and Webots controller
├── training/ # NEW
│ ├── herding_env.py # gymnasium.Env, single-agent (the dog)
│ ├── parity_test.py # asserts env trajectory ≈ Webots trajectory for fixed seeds
│ ├── train_ppo.py # SB3 PPO entry point
│ ├── eval.py # rollout + metrics (success rate, time-to-pen)
│ ├── configs/
│ │ ├── ppo_default.yaml
│ │ └── curriculum.yaml
│ ├── runs/ # tensorboard + checkpoints (.gitignored)
│ └── requirements.txt
├── docs/
│ └── project.md # unchanged
├── plan.md # this file
└── ...
```
`herding/` becomes the **single source of truth** for geometry and dynamics.
The Webots controllers and the training env both import from it, so when a
constant changes in one place it changes everywhere — eliminating the
sim/Webots-drift class of bugs.
This means the existing `controllers/sheep/flocking.py` and
`controllers/shepherd_dog/strombom.py` become thin shims that re-export
from `herding/`. Webots controllers can import `herding/` because Webots
adds the project root to `sys.path` at controller startup; we'll verify.
---
## 4. The Gymnasium environment — `training/herding_env.py`
```python
class HerdingEnv(gymnasium.Env):
metadata = {"render_modes": ["rgb_array", "human"]}
def __init__(self, n_sheep=3, max_steps=3000, dt=0.016, seed=None):
self.action_space = Box(low=-1, high=1, shape=(2,), dtype=np.float32)
self.observation_space = Box(low=-1, high=1, shape=(28,), dtype=np.float32)
...
def reset(self, *, seed=None, options=None):
# Random sheep positions in field \ pen corridor, dog near origin.
# Optional curriculum: options["n_sheep"] overrides.
...
def step(self, action):
vx, vy = action # high-level velocity intent
# Convert to wheel speeds via the same drive_action inverse used in Webots
wL, wR = self._diffdrive_inverse(vx, vy, self.dog_state)
self.dog_state = self._integrate_diffdrive(self.dog_state, wL, wR, self.dt)
# Step every sheep one boid step (vectorized in flocking_sim.py)
self.sheep_state = self._step_sheep(self.sheep_state, self.dog_state)
# Update penned set, compute reward, observation, done flags
...
```
Key points:
- **Vectorised sheep update**: re-implements `flocking.py` in numpy so 100
parallel envs with 5 sheep each take ms, not seconds. Numerical parity
with the scalar version is asserted in `parity_test.py`.
- **Same diff-drive integrator** for the dog as Webots will see at
inference. Wall + pen-fence collisions clamp position (a Webots-realistic
no-pass-through approximation).
- **Domain randomization** in reset: sheep count, spawn positions, sheep
flock-parameter jitter (±10 % on FLEE_DIST, COHESION_DIST, etc.) for
robustness.
---
## 5. Training pipeline — `training/train_ppo.py`
- **Algorithm**: SB3 `PPO` with `MlpPolicy`, `n_steps=2048`, `batch_size=256`,
`n_epochs=10`, `gamma=0.995`, `gae_lambda=0.95`, `clip_range=0.2`,
`ent_coef=0.005`, `vf_coef=0.5`, `learning_rate=3e-4`.
- **Vec envs**: `SubprocVecEnv` × 16 parallel envs (the env is pure numpy
so subprocs are CPU-cheap).
- **Normalization**: `VecNormalize(norm_obs=True, norm_reward=True,
clip_obs=10.0)`. Pickled alongside the policy zip — both required at
inference.
- **Callbacks**:
- `CheckpointCallback` every 100 k steps.
- `EvalCallback` on a separate eval env (no normalization-update) every
50 k steps; logs success rate and time-to-pen to TensorBoard.
- Custom `CurriculumCallback`: bumps `n_sheep` when eval success rate
crosses the stage threshold for 3 consecutive evals.
- **Determinism for debugging**: seed-pinned eval env so regressions are
catchable.
---
## 6. Webots integration — RL inference path
`controllers/shepherd_dog/shepherd_dog.py` becomes a thin wrapper:
```python
MODE = os.environ.get("HERDING_MODE", "rl") # "rl" | "strombom"
if MODE == "rl":
policy = policy_loader.load("training/runs/best/policy.zip",
"training/runs/best/vecnormalize.pkl")
obs_fn = build_obs # from herding/obs.py
else:
obs_fn = None # strombom path uses sheep_positions directly
while robot.step(timestep) != -1:
receive_messages()
if MODE == "rl":
obs = obs_fn(dog_xy, dog_heading, sheep_positions, ...)
action, _ = policy.predict(obs, deterministic=True)
vx, vy = action.tolist()
else:
vx, vy, mode, dbg = compute_action_debug(dog_xy, sheep_positions, PEN_ENTRY)
# plus existing rescue/cooldown/EMA layer
drive_action(vx, vy, ...)
```
A **safety supervisor** wraps the RL output: if `obs` indicates the dog is
< 0.6 m from a wall, override with the existing wall-escape behavior
(reverse + turn). This is a hard guarantee diff-drive needs because PPO
may not discover wall-escape reliably from on-policy data.
`policy_loader.py` handles the SB3 import lazily so the controller still
works with `MODE=strombom` even if SB3 is not installed in the Webots
Python environment.
---
## 7. Optional extensions (post-baseline)
- **External pen** (Section 2.1 option B): edit `field.wbt` to extend the
south wall hole into an external L-shaped pen with its own walls; update
`herding/geometry.py`; retrain stage 3 only.
- **Lidar observation**: replace symbolic obs with 36-bin downsampled
lidar + ego state; train end-to-end. Useful as the "extra merit"
dimension in the project doc.
- **Two-dog mode**: make env multi-agent, train with `MAPPO`-style shared
critic or independent PPO. The proto already supports multiple dog
instances; world only needs a second `ShepherdDog` node.
- **Mecanum comparison**: swap the dog proto for a mecanum variant; same
policy, different `_integrate_diffdrive` (becomes holonomic).
- **Sheep flock size scaling**: 5, 10, 20 — the obs is order-invariant so
the same policy generalises; just curriculum further.
---
## 8. Risks & mitigations
| Risk | Mitigation |
|---|---|
| Sim-to-Webots gap (sheep dynamics, wall friction) | `parity_test.py` asserts trajectory match within tolerance for fixed seeds; if it fails, fix the env, not the policy |
| Dog learns to wall-pin sheep against fence | Add `w_collide` penalty + min-sheep-to-wall term in obs; curriculum from 1 sheep first |
| PPO oscillation collapses into spinning | Action smoothing in env step (EMA on `(vx, vy)`, mirroring `ACTION_SMOOTH=0.35` from Strömbom controller); reward small `‖a_t − a_{t-1}‖` penalty |
| Pen approach failures (sheep refuse gate) | Reuse the existing `deadzone_rescue` as a *scripted fallback* triggered when a sheep has been deadzoned > 200 steps — RL handles the common case, scripted handles the corner |
| Gym version mismatch (gymnasium vs gym) | Lock to `gymnasium>=0.29`, `stable-baselines3>=2.3` in requirements |
---
## 9. Milestones (suggested order of implementation)
1. **M0 — Refactor** (no behavior change): create `herding/` package, move
constants out of `flocking.py`/`strombom.py`, leave shims; verify
Webots still runs Strömbom unchanged. Add `dog_behavior_log.csv` to
`.gitignore`.
2. **M1 — Env & parity**: `herding_env.py`, `parity_test.py`. Asserts
sheep + dog trajectories match Webots within tolerance for 5 fixed
seeds. *Done when parity test green.*
3. **M2 — PPO baseline**: train Stage 0 (1 sheep) for 0.5 M steps; eval
in env at ≥ 90 % success.
4. **M3 — Webots inference**: load Stage 0 policy in `shepherd_dog.py`
with `HERDING_MODE=rl`; verify the dog herds 1 sheep into the pen in
the actual Webots world. *This is the sim-to-sim transfer gate.*
5. **M4 — Curriculum**: stages 1–3, ~5 M steps total, with checkpoints
and eval logs.
6. **M5 — Strömbom comparison**: run both controllers on a fixed eval
suite (same seeds, 1/2/3 sheep), log success rate and time-to-pen.
This is a deliverable for the project's "quantitative evaluation"
goal.
7. **M6 — Documentation**: a short README in `training/` showing how to
train, evaluate, and switch modes in Webots.
Each milestone is independently demoable. M0–M3 is the critical path to
"RL works in Webots"; M4–M6 polishes it for the project deliverable.
---
## 10. Decisions (locked in by implementation)
- **Pen layout**: option B (external pen). The pen sits south of the
field at x ∈ [10, 13], y ∈ [-22, -15] and is reached through the
existing 3 m gap in the south stone wall. The old in-field
quarantine fence is gone and the wooden gate is modeled as
swung-open and parked on the west gate post so the corridor is
unobstructed. This kills the deadzone class entirely.
- **Flock size**: 1..10 sheep, sampled uniformly each reset. The order-
invariant observation (CoM, dispersion, polar histogram) lets a
single policy generalise across the whole range. A curriculum widens
``max_n_sheep`` from 1 to 10 over training to keep early exploration
tractable.
- **Single-sheep mode**: handled by the same policy (n_sheep=1 is the
first stage of the curriculum and stays in the training distribution
throughout). No separate model.
- **Hardware**: GPU for training. SubprocVecEnv × 16 on CPU feeds an
MlpPolicy on GPU; ~2–3 h for the full curriculum.
## 11. What was built
```
herding/ # single source of truth, importable from both
geometry.py # field/pen constants, latch helpers, robot specs
flocking_sim.py # Reynolds boid step (matches Webots controller)
diffdrive.py # diff-drive kinematics + velocity↔wheels
obs.py # 28-D order-invariant observation builder
strombom.py # collect/drive heuristic (baseline + fallback)
worlds/field.wbt # external pen south of field, 10 sheep slots,
# gate parked open, in-field fence removed
controllers/sheep/sheep.py # imports from herding/, latches on
# is_penned_position
controllers/shepherd_dog/
shepherd_dog.py # mode switch (HERDING_MODE=rl|strombom),
# safety supervisor for DOG_SOUTH_LIMIT
policy_loader.py # lazy SB3 zip + VecNormalize loader
strombom.py # shim re-exporting herding.strombom
training/
herding_env.py # gymnasium.Env, action smoothing, reward shaping
train_ppo.py # SB3 PPO with VecNormalize, eval, checkpoints,
# curriculum callback
eval.py # success-rate / time-to-pen across n_sheep
parity_test.py # shape, determinism, baseline-rollout smoke test
configs/ppo_default.yaml
requirements.txt
README.md # how to train, evaluate, switch modes in Webots
```
## 12. To run
```bash
# 1. Install deps (CUDA-enabled torch wheel for GPU)
pip install -r training/requirements.txt
# 2. Smoke test
python -m training.parity_test
# 3. Train (5 M steps, ~2–3 h on a single GPU)
python -m training.train_ppo --out-dir training/runs/baseline
# 4. Evaluate vs Strömbom
python -m training.eval --policy training/runs/baseline/best
python -m training.eval --policy strombom
# 5. Run in Webots
export HERDING_MODE=rl
export HERDING_POLICY_DIR=$PWD/training/runs/baseline/best
webots worlds/field.wbt
```
tools/camera_debug.py
-22
View File
@@ -1,22 +0,0 @@
"""
Viewpoint inspector — prints position, orientation and FOV to the console
once per second. Attach as the controller of a dummy supervisor robot to
copy-paste exact camera values into field.wbt.
"""
from controller import Supervisor
robot = Supervisor()
timestep = int(robot.getBasicTimeStep())
vp = robot.getFromDef("VIEWPOINT")
step = 0
while robot.step(timestep) != -1:
if step % 60 == 0:
pos = vp.getField("position").getSFVec3f()
ori = vp.getField("orientation").getSFRotation()
fov = vp.getField("fieldOfView").getSFFloat()
print(f"position: {pos[0]:.3f} {pos[1]:.3f} {pos[2]:.3f}")
print(f"orientation: {ori[0]:.3f} {ori[1]:.3f} {ori[2]:.3f} {ori[3]:.3f}")
print(f"fieldOfView: {fov:.3f}\n")
step += 1
tools/collect_demos.py
+17 -4
View File
@@ -27,11 +27,19 @@ if _PROJECT_ROOT not in sys.path:
import numpy as np
from herding.geometry import PEN_ENTRY
from herding.sequential import compute_action
from herding.sequential import compute_action as sequential_action
from herding.strombom import compute_action as strombom_action
from training.herding_env import HerdingEnv
def collect_one(n_sheep: int, seed: int, max_steps: int, subsample: int):
TEACHERS = {
"sequential": sequential_action,
"strombom": strombom_action,
}
def collect_one(n_sheep: int, seed: int, max_steps: int, subsample: int,
teacher_fn):
env = HerdingEnv(n_sheep=n_sheep, max_steps=max_steps,
difficulty=1.0, seed=seed)
obs, _ = env.reset(seed=seed)
@@ -41,7 +49,7 @@ def collect_one(n_sheep: int, seed: int, max_steps: int, subsample: int):
for i in range(env.n_sheep) if not env.sheep_penned[i]}
if not positions:
break
vx, vy, _mode = compute_action(
vx, vy, _mode = teacher_fn(
(env.dog_x, env.dog_y), positions, PEN_ENTRY,
)
action = np.array([vx, vy], dtype=np.float32)
@@ -70,7 +78,12 @@ def main():
help="Keep every Nth (obs, action) pair.")
parser.add_argument("--keep-failures", action="store_true",
help="Include partial-success trajectories. Default off.")
parser.add_argument("--teacher", default="sequential",
choices=list(TEACHERS.keys()),
help="Which analytic teacher to demonstrate.")
args = parser.parse_args()
teacher_fn = TEACHERS[args.teacher]
print(f"[demos] teacher: {args.teacher}")
n_sheep_list = [int(x) for x in args.n_sheep_list.split(",")]
print(f"[demos] grid: n_sheep={n_sheep_list}, seeds={args.seeds_per_n}, "
@@ -83,7 +96,7 @@ def main():
for n in n_sheep_list:
for seed in range(args.seeds_per_n):
obs, actions, success, total_steps = collect_one(
n, seed, args.max_steps, args.subsample,
n, seed, args.max_steps, args.subsample, teacher_fn,
)
n_total += 1
if success:
tools/run_webots.sh
+3 -3
View File
@@ -15,7 +15,7 @@
# tools/run_webots.sh 3 strombom # canonical baseline, 3 sheep
#
# Notes:
# * The RL mode loads training/runs/bc_pretrained/policy.zip by default.
# * The RL mode loads training/runs/bc_solo/policy.zip by default.
# Override via HERDING_POLICY_DIR=/path/to/run env var.
# * Conda env "tir" must be active (provides stable-baselines3 + torch).
@@ -46,12 +46,12 @@ echo "------------------------------------------------------------"
echo "World : $DST"
echo "Mode : $MODE"
echo "Sheep : $active active"
echo "Policy dir : ${HERDING_POLICY_DIR:-$ROOT/training/runs/bc_pretrained}"
echo "Policy dir : ${HERDING_POLICY_DIR:-$ROOT/training/runs/bc_solo}"
echo "------------------------------------------------------------"
# Webots strips HERDING_* env vars from controller subprocesses in some
# setups, so we also write a runtime config file the controller reads.
RESOLVED_POLICY_DIR="${HERDING_POLICY_DIR:-$ROOT/training/runs/bc_pretrained}"
RESOLVED_POLICY_DIR="${HERDING_POLICY_DIR:-$ROOT/training/runs/bc_solo}"
cat > "$ROOT/herding_runtime.cfg" <<EOF
HERDING_MODE=$MODE
HERDING_POLICY_DIR=$RESOLVED_POLICY_DIR
training/README.md
+70 -89
View File
@@ -1,115 +1,96 @@
# Shepherd Herding — Training & Inference
# Training pipeline
This directory holds the Gymnasium environment, PPO training script, and
evaluation harness for the RL shepherd-dog policy. The Webots controller
in `controllers/shepherd_dog/` loads the resulting policy at inference
time when launched with `HERDING_MODE=rl`.
Behavior cloning of analytic herding teachers into a neural network
policy that runs in Webots. PPO from scratch and PPO fine-tune of BC
were tried earlier and are kept under `train_ppo.py` as experimental
options, but the BC route alone is what we ship.
## Layout
## Files
```
training/
├── herding_env.py # gymnasium.Env — the dog is the agent
├── train_ppo.py # SB3 PPO entry point (vec envs, eval, curriculum)
├── eval.py # rollout success-rate / time-to-pen across flock sizes
├── parity_test.py # smoke test: shapes, determinism, baseline rollout
├── configs/ppo_default.yaml
├── runs/ # tensorboard + checkpoints (gitignored)
└── requirements.txt
herding_env.py — Gymnasium env (used for demo collection + eval)
bc_pretrain.py — supervised MSE+cosine training of an SB3 MlpPolicy
against (obs, action) demos
eval.py — analytic teachers + BC policies, full n=1..10 grid
parity_test.py — shape/determinism/baseline smoke test
train_ppo.py — PPO trainer (experimental — see Appendix below)
configs/ — PPO hyperparameter YAML
runs/ — checkpoints (.gitignored)
```
## Setup
```bash
python -m venv .venv && source .venv/bin/activate
pip install -r training/requirements.txt
```
pip install -r requirements.txt
```
CPU is the default and also the recommended device — SB3's PPO with an
MLP policy of this size runs faster on CPU than on GPU because the
bottleneck is rollout collection, not gradient compute. The 16 SubprocVecEnv
workers saturate ~16 CPU cores. To force CUDA anyway, pass `--device cuda`.
CPU is the default and recommended device — SB3 PPO with an MLP policy
of this size runs faster on CPU than GPU because the bottleneck is
rollout collection, not gradient compute.
## Train
## The BC pipeline
```bash
# Full curriculum (1 → 10 sheep), ~5M steps, ~2–3h on a single GPU.
python -m training.train_ppo \
--config training/configs/ppo_default.yaml \
--out-dir training/runs/baseline
```
# 1. Generate demos from an analytic teacher.
# --teacher: strombom (default), sequential, drive_only, hybrid, strombom_smooth
python -m tools.collect_demos --teacher strombom \
--out demos.npz --seeds-per-n 30 --subsample 3
# 2. Behavior-clone the demos into an MLP policy.
python -m training.bc_pretrain --demos demos.npz \
--out runs/bc_flock --epochs 100 --net-arch 512,512
# 3. Evaluate the resulting policy.
python -m training.eval --policy runs/bc_flock \
--max-flock 10 --max-steps 30000 --n-seeds 5
```
Outputs:
- `training/runs/baseline/best/best_model.zip` — best eval checkpoint
- `training/runs/baseline/best/vecnormalize.pkl` — observation stats
- `training/runs/baseline/checkpoints/ppo_*.zip` — periodic checkpoints
- `training/runs/baseline/tb/` — TensorBoard logs (`tensorboard --logdir`)
Wall time: ~10 min demos + ~5 min BC training + ~5 min eval.
To resume:
`bc_pretrain.py` saves the **best-val_cos** snapshot, not the final
epoch — multi-modal teachers (Strömbom's collect/drive switch) make
training noisy and the last epoch is often worse than an earlier one.
```bash
python -m training.train_ppo --resume training/runs/baseline/checkpoints/ppo_500000_steps.zip
## Available analytic teachers
| Name | What it does | Best for |
|---|---|---|
| `strombom` | Canonical Strömbom — collect when flock is scattered, drive CoM otherwise | Tight-cohesion regime, n=1-10 |
| `sequential` | Pick the sheep closest to the pen and drive only it | Loose-cohesion regime, n=1-10 |
| `drive_only` | Strömbom drive without collect mode (continuous action) | Easier-to-BC alternative; less reliable than full Strömbom |
| `hybrid` | Drive rearmost sheep when far, switch to closest near gate | Failed experiment, kept for write-up |
| `strombom_smooth` | Sigmoid-blended Strömbom collect↔drive | Failed experiment |
## Evaluating the analytic teachers directly
```
python -m training.eval --policy strombom --max-flock 10 --max-steps 30000 --n-seeds 5
python -m training.eval --policy sequential --max-flock 10 --max-steps 30000 --n-seeds 5
```
## Evaluate
## Webots inference
```bash
# RL policy
python -m training.eval --policy training/runs/baseline/best
The Webots dog controller (`controllers/shepherd_dog/shepherd_dog.py`)
loads a saved BC zip when launched in `rl` mode:
# Strömbom baseline
python -m training.eval --policy strombom
```
HERDING_POLICY_DIR=$PWD/runs/bc_flock tools/run_webots.sh 10 rl
```
Prints success rate, mean steps, and mean penned-count per flock size.
Use the same `--n-seeds` for both to get a fair RL-vs-Strömbom A/B.
It auto-discovers a checkpoint named `policy.zip`, `best_model.zip`, or
`final.zip` in the directory.
## Parity / smoke test
## Appendix — experimental PPO scripts
```bash
python -m training.parity_test
```
`train_ppo.py` contains the PPO/RL pipeline tried before BC:
* PPO from scratch with curriculum learning over flock size + spawn area.
* PPO fine-tune of a BC checkpoint.
Checks observation/action shapes, deterministic seeding, the curriculum
sampler, and a 400-step Strömbom rollout. Run this before every long
training job — catches the boring class of bugs in seconds.
Both ran into stability issues (PPO's exploration noise destroys BC
weights faster than the reward signal can rebuild them; PPO from
scratch never sees pen events often enough during random exploration to
credit-assign the +500 done bonus).
## Run the policy in Webots
1. Train (above) — produces `training/runs/<name>/best/`.
2. In Webots, set the dog controller's environment variables:
```bash
export HERDING_MODE=rl
export HERDING_POLICY_DIR=$(pwd)/training/runs/baseline/best
webots worlds/field.wbt
```
Or set them via Webots' controller args / a `.wbproj` if you prefer.
3. To force the Strömbom baseline (same world, same controller):
```bash
export HERDING_MODE=strombom
webots worlds/field.wbt
```
If `HERDING_MODE=rl` but the policy can't be loaded (SB3 not installed,
zip missing, etc.), the controller logs the error and falls back to
Strömbom automatically.
## Curriculum knobs
The default schedule in `configs/ppo_default.yaml` widens
`max_n_sheep` over training. Each reset samples `n_sheep ~ U[1,
max_n_sheep]`, so the final policy has seen every flock size from 1 to
10 in proportion. To pin a specific size, instantiate the env with
`HerdingEnv(n_sheep=N)` (see `eval.py`).
## Reward shaping
Weights live in class attributes on `HerdingEnv`. Tune from the 1-sheep
curriculum first — if the dog can't herd a single sheep cleanly, raising
`W_PROGRESS` or lowering `W_TIME` is usually the fix. For multi-sheep
collapse modes (dog spins between sheep), increase `W_COMPACT` so
tightening the flock pays.
The script is left in place because the abstractions are sound and the
code is reusable for follow-up work (e.g. KL-regularised fine-tune
with a frozen reference policy). Not part of the deliverable pipeline.
training/bc_pretrain.py
+25 -11
View File
@@ -1,20 +1,21 @@
"""Behavior cloning of the sequential teacher into an SB3-compatible policy.
"""Behavior cloning of an analytic teacher into an SB3-compatible policy.
Trains the policy network (mean-action head) of an SB3 ``MlpPolicy`` to
mimic the demonstrations collected by ``tools.collect_demos``. The
saved zip is loadable via ``PPO.load(...)`` and can be passed to
``train_ppo.py --resume`` for fine-tuning.
Trains the policy network (mean-action head) of an SB3 ``MlpPolicy``
to mimic the (obs, action) demonstrations produced by
``tools.collect_demos``. The saved zip is loadable via ``PPO.load(...)``
and is what the Webots dog controller uses in ``HERDING_MODE=rl``.
Why this works: the teacher (sequential single-target driving) solves
n=10 at 80%+ in our env. BC gives the RL a competent starting policy,
so PPO doesn't have to discover behavior from scratch — it only has to
*refine* the teacher's strategy via the sparse pen reward.
Loss: MSE + (1 - cosine similarity). The cosine term is what stops
the policy mean from collapsing toward zero against unit-vector
targets. Best-by-val_cos checkpoint is restored at the end of training
so noisy multi-modal teachers (e.g. Strömbom) don't lose progress when
the last epoch lands on a bad gradient step.
Usage::
python -m training.bc_pretrain \\
--demos training/demos.npz \\
--out training/runs/bc_pretrained
--out training/runs/bc_flock
"""
from __future__ import annotations
@@ -80,7 +81,7 @@ def policy_forward_mean(policy, obs_batch):
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--demos", default="training/demos.npz")
parser.add_argument("--out", default="training/runs/bc_pretrained")
parser.add_argument("--out", default="training/runs/bc_solo")
parser.add_argument("--epochs", type=int, default=60)
parser.add_argument("--batch-size", type=int, default=256)
parser.add_argument("--lr", type=float, default=1e-3)
@@ -147,6 +148,11 @@ def main():
f"lr={args.lr} device={args.device}")
t_start = time.time()
best_val = float("inf")
best_cos = -1.0
# Snapshot the best-by-val_cos policy weights and restore at the end —
# training is noisy on multi-modal teachers (e.g. Strömbom collect/drive),
# so the last epoch is often worse than an earlier one.
best_state = None
def combined_loss(pred, target):
mse = nn.functional.mse_loss(pred, target)
@@ -201,6 +207,14 @@ def main():
f"val_mse={val_mse:.4f} val_cos={cos_sim:+.3f}")
if val_mse < best_val:
best_val = val_mse
if cos_sim > best_cos:
best_cos = cos_sim
best_state = {k: v.detach().cpu().clone()
for k, v in policy.state_dict().items()}
if best_state is not None:
policy.load_state_dict(best_state)
print(f"[bc] restored best-val_cos snapshot (cos={best_cos:.3f})")
elapsed = time.time() - t_start
print(f"[bc] done in {elapsed:.0f}s best_val_mse={best_val:.4f}")
training/demos.npz
BIN
View File
Binary file not shown.
training/eval.py
+1 -1
View File
@@ -26,8 +26,8 @@ if _PROJECT_ROOT not in sys.path:
import numpy as np
from herding.geometry import MAX_SHEEP, PEN_ENTRY
from herding.strombom import compute_action as strombom_action
from herding.sequential import compute_action as sequential_action
from herding.strombom import compute_action as strombom_action
from training.herding_env import HerdingEnv
training/runs/bc_flock/policy.zip
BIN
View File
Binary file not shown.
training/runs/bc_solo/policy.zip
BIN
View File
Binary file not shown.
training/train_ppo.py
+21 -8
View File
@@ -1,18 +1,31 @@
"""Train a PPO shepherd-dog policy on ``HerdingEnv`` with curriculum.
"""PPO trainer for the shepherd-dog policy — EXPERIMENTAL.
Defaults to 16 parallel ``SubprocVecEnv`` workers feeding a GPU policy.
Saves checkpoints, the best-eval model, and the VecNormalize stats —
all three are needed at inference time by the Webots controller.
The deliverable pipeline is `bc_pretrain.py` (see ``training/README.md``).
This script is kept in the tree because it implements:
Usage::
* PPO from scratch with curriculum over flock size + spawn area, and
* PPO fine-tune of a behavior-cloned policy.
Both ran into stability issues in our setting (long-horizon credit
assignment for sparse pen reward, BC-degradation under PPO exploration
noise). The abstractions are reusable for follow-up work — e.g.
KL-regularised fine-tune with a frozen reference policy — so we leave
the code in place.
Usage (PPO from scratch)::
python -m training.train_ppo \
--config training/configs/ppo_default.yaml \
--out-dir training/runs/baseline
--out-dir training/runs/ppo_scratch
To resume from a checkpoint::
Usage (PPO fine-tune of BC)::
python -m training.train_ppo --resume training/runs/baseline/checkpoints/ppo_500000_steps.zip
python -m training.train_ppo \
--resume training/runs/bc_flock/policy.zip \
--out-dir training/runs/bc_ppo \
--no-vecnorm --no-curriculum --imitate-weight 0 \
--difficulty 1.0 --log-std -1.5 --learning-rate 5e-5 \
--total-timesteps 3000000
"""
from __future__ import annotations
worlds/.field.wbproj
-9
View File
@@ -1,9 +0,0 @@
Webots Project File version R2025a
perspectives: 000000ff00000000fd00000002000000010000011c000001bcfc0200000001fb0000001400540065007800740045006400690074006f00720100000000000001bc0000003f00ffffff00000003000005c600000220fc0100000001fb0000001a0043006f006e0073006f006c00650041006c006c0041006c006c0100000000000005c60000006900ffffff000004a8000001bc00000001000000020000000100000008fc00000000
simulationViewPerspectives: 000000ff000000010000000200000100000006250100000002010000000100
sceneTreePerspectives: 000000ff00000001000000030000001f000000c0000000000100000002010000000200
maximizedDockId: -1
centralWidgetVisible: 1
orthographicViewHeight: 1
textFiles: -1
consoles: Console:All:All