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Re-expresses ``V(state)`` -- the expected discounted intent-shaped return -- as
a dense ``torch.nn.Parameter`` table of shape ``[table_size]`` so the TD(0) and
Monte-Carlo updates become single tensor ops, and gradients flow through ``V``
to whatever outer loss consumes it.
Layout
------
The value table is indexed by the same SHA-256 -> integer row hash used by the
other ``*_tensor.py`` companions (see :mod:`causal_exploration_tensor`):
index = int.from_bytes(sha256(state_key).digest()[:8], 'little') % table_size
Collisions are acceptable (hash-bucketed). Visit counts are a non-learned
buffer so they move with ``.to(device)`` but take no gradients; the value table
itself is a learnable :class:`torch.nn.Parameter`.
Updates
-------
* :meth:`TensorValueFunction.update_td_value` -- the TD(0) rule as a differentiable
tensor op: ``V[s] + alpha * (r + gamma * V[s'] - V[s])``. Returns the new
``V[s]`` tensor (caller may use it as a loss target or for the TD error).
* :meth:`TensorValueFunction.update_mc_value` -- the running-mean MC rule.
* :meth:`TensorValueFunction.update_mc_trajectory_value` -- fully vectorized
discounted return-to-go via ``torch.cumprod`` / ``torch.cumsum`` (no Python
loops), returning a length-``T`` tensor of per-step MC targets.
The original :mod:`resynthesis.value_function` module is preserved as the
torch-free reference; this companion is additive and importable independently.
"""
from __future__ import annotations
from collections.abc import Sequence
import torch
from torch import Tensor, nn
from resynthesis.causal_exploration_tensor import state_hash_index, state_hash_indices
VALUE_FUNCTION_TENSOR_SCHEMA = "nnf.resynthesis.value_function_tensor.v1"
DEFAULT_VALUE_DISCOUNT = 0.9
DEFAULT_TD_ALPHA = 0.1
DEFAULT_TABLE_SIZE = 4096
def discounted_returns(rewards: Tensor, *, discount: float) -> Tensor:
"""Vectorized discounted return-to-go ``G_t = sum_k gamma^(k-t) r_k``.
``rewards`` is a 1-D ``[T]`` tensor. Returns ``[T]`` where entry ``t`` is
the discounted sum of rewards from ``t`` onward. Built via an
upper-triangular weight matrix ``M[t, k] = gamma^(k-t)`` (looked up from
the discount-power vector by integer offset ``k - t``) for ``k >= t``;
the returns are ``M @ rewards`` -- single matmul, fully differentiable, no
Python loops. Correct when ``discount == 0`` (only the immediate reward
counts at each step).
"""
if rewards.dim() != 1:
raise ValueError("rewards must be 1-D")
length = int(rewards.shape[0])
if length == 0:
return rewards
powers = torch.tensor(
[discount ** i for i in range(length)],
dtype=rewards.dtype,
device=rewards.device,
)
idx = torch.arange(length, device=rewards.device, dtype=torch.long)
exponent = idx.unsqueeze(0) - idx.unsqueeze(1) # [t, k]
lookup = torch.clamp(exponent, min=0)
m = powers[lookup]
upper = exponent >= 0
m = torch.where(upper, m, torch.zeros_like(m))
return m @ rewards
class TensorValueFunction(nn.Module):
"""Per-state expected discounted intent return as a dense value table.
The value table ``V`` is a learnable ``[table_size]`` parameter; visit
counts are a buffer (no gradients). All updates and lookups are tensor ops.
"""
value_table: Tensor
visit_counts: Tensor
def __init__(
self,
*,
table_size: int = DEFAULT_TABLE_SIZE,
discount: float = DEFAULT_VALUE_DISCOUNT,
device: str | torch.device | None = None,
dtype: torch.dtype = torch.float32,
) -> None:
super().__init__()
if table_size <= 0:
raise ValueError("table_size must be positive")
self.table_size = int(table_size)
self.discount = float(discount)
self.dtype = dtype
self.value_table = nn.Parameter(
torch.zeros(self.table_size, dtype=dtype, device=device)
)
self.register_buffer(
"visit_counts",
torch.zeros(self.table_size, dtype=dtype, device=device),
)
# ------------------------------------------------------------------
# device helpers
# ------------------------------------------------------------------
@property
def device(self) -> torch.device:
return self.value_table.device
# ------------------------------------------------------------------
# indexing
# ------------------------------------------------------------------
def _row(self, state_key: str) -> int:
return state_hash_index(state_key, table_size=self.table_size)
def _rows(self, state_keys: Sequence[str]) -> Tensor:
return state_hash_indices(state_keys, table_size=self.table_size).to(self.device)
# ------------------------------------------------------------------
# evaluation
# ------------------------------------------------------------------
def evaluate(self, state_key: str) -> Tensor:
"""Scalar tensor ``V(state_key)`` (gradient-flowing)."""
return self.value_table[self._row(state_key)]
def evaluate_many(self, state_keys: Sequence[str]) -> Tensor:
"""Vectorized ``[N]`` evaluation (one ``index_select`` kernel)."""
rows = self._rows(state_keys)
return self.value_table[rows]
def visits(self, state_key: str) -> Tensor:
return self.visit_counts[self._row(state_key)]
def best_state(self, state_keys: Sequence[str]) -> int:
"""Index (into ``state_keys``) of the highest-``V`` candidate.
Returns ``-1`` for an empty sequence. Returns an ``int`` (``argmax`` is
not differentiable through the index choice itself, but the underlying
values are gradient-flowing -- use :meth:`evaluate_many` directly if you
need a differentiable reduction).
"""
if not state_keys:
return -1
values = self.evaluate_many(state_keys)
return int(torch.argmax(values).item())
# ------------------------------------------------------------------
# TD(0) update (differentiable tensor op)
# ------------------------------------------------------------------
def td_error(
self,
*,
reward: float | Tensor,
state_key: str,
next_state_key: str,
discount: float | None = None,
) -> Tensor:
gamma = self.discount if discount is None else float(discount)
target = torch.as_tensor(reward, dtype=self.dtype, device=self.device) + gamma * self.evaluate(next_state_key)
return target - self.evaluate(state_key)
def update_td_value(
self,
*,
reward: float | Tensor,
state_key: str,
next_state_key: str,
alpha: float = DEFAULT_TD_ALPHA,
discount: float | None = None,
) -> Tensor:
"""Differentiable TD(0) update op.
Returns the new ``V(state_key)`` *as a tensor* (so the caller can form a
loss). Note: because ``V`` is a parameter, the actual on-table update is
applied via ``index_add_`` on the underlying parameter data inside
``torch.no_grad()`` -- this module is meant to be the *target* of an
outer optimizer step, not an in-place learning rule. For the classic
in-place rule use :meth:`apply_td_update`.
"""
error = self.td_error(
reward=reward,
state_key=state_key,
next_state_key=next_state_key,
discount=discount,
)
return self.evaluate(state_key) + alpha * error
def apply_td_update(
self,
*,
reward: float | Tensor,
state_key: str,
next_state_key: str,
alpha: float = DEFAULT_TD_ALPHA,
discount: float | None = None,
) -> Tensor:
"""In-place classic TD(0) update on the parameter; returns TD error.
Mirrors :meth:`resynthesis.value_function.ValueFunction.update_td`:
``V[s] += alpha * (r + gamma*V[s'] - V[s])`` and the visit count bumps.
Done under ``no_grad`` because this is a tabular learning rule, not a
gradient-step target.
"""
error = self.td_error(
reward=reward,
state_key=state_key,
next_state_key=next_state_key,
discount=discount,
)
row = self._row(state_key)
with torch.no_grad():
self.value_table[row] += alpha * error
self.visit_counts[row] += 1.0
return error
def apply_td_update_batch(
self,
*,
state_keys: Sequence[str],
next_state_keys: Sequence[str],
rewards: Tensor,
alpha: float = DEFAULT_TD_ALPHA,
discount: float | None = None,
) -> Tensor:
"""Vectorized in-place TD(0) over a batch of transitions.
``rewards`` is ``[N]``; ``state_keys`` / ``next_state_keys`` are
length-``N``. Returns the ``[N]`` TD errors. One ``index_add_`` per
tensor -- no Python loop over the batch.
"""
n = len(state_keys)
if len(next_state_keys) != n or int(rewards.shape[0]) != n:
raise ValueError("state_keys / next_state_keys / rewards length mismatch")
gamma = self.discount if discount is None else float(discount)
rows = self._rows(state_keys)
next_rows = self._rows(next_state_keys)
rew = rewards.to(self.device).to(self.dtype)
v = self.value_table
targets = rew + gamma * v[next_rows]
errors = targets - v[rows]
with torch.no_grad():
self.value_table.index_add_(
0, rows, alpha * errors
)
ones = torch.ones(n, dtype=self.dtype, device=self.device)
self.visit_counts.index_add_(0, rows, ones)
return errors
# ------------------------------------------------------------------
# Monte-Carlo update
# ------------------------------------------------------------------
def update_mc_value(
self,
*,
state_key: str,
return_value: float | Tensor,
) -> Tensor:
"""Differentiable running-mean MC update op (returns the new V[s])."""
count = self.visits(state_key)
prior = self.evaluate(state_key)
return (prior * count + torch.as_tensor(return_value, dtype=self.dtype, device=self.device)) / (count + 1.0)
def apply_mc_update(
self,
*,
state_key: str,
return_value: float | Tensor,
) -> Tensor:
"""In-place running-mean MC update; returns the new ``V[state_key]``."""
row = self._row(state_key)
count = self.visit_counts[row]
prior = self.value_table[row]
ret = torch.as_tensor(return_value, dtype=self.dtype, device=self.device)
new_value = (prior * count + ret) / (count + 1.0)
with torch.no_grad():
self.value_table[row] = new_value
self.visit_counts[row] += 1.0
return new_value
def update_mc_trajectory_value(
self,
state_keys: Sequence[str],
rewards: Tensor,
*,
discount: float | None = None,
) -> Tensor:
"""Differentiable MC return-to-go over a trajectory.
Returns the ``[T]`` tensor of per-step MC returns
``G_t = sum_k gamma^(k-t) rewards[k]`` (the *targets* for an outer
optimizer step on ``V``). Fully vectorized via
:func:`discounted_returns` -- no Python loops.
"""
if len(state_keys) != int(rewards.shape[0]):
raise ValueError("state_keys and rewards length mismatch")
gamma = self.discount if discount is None else float(discount)
rewards = rewards.to(self.device).to(self.dtype)
return discounted_returns(rewards, discount=gamma)
def apply_mc_trajectory(
self,
state_keys: Sequence[str],
rewards: Tensor,
*,
discount: float | None = None,
) -> None:
"""In-place MC running-mean update for every state on the trajectory."""
targets = self.update_mc_trajectory_value(
state_keys, rewards, discount=discount
)
rows = self._rows(state_keys)
counts = self.visit_counts[rows]
priors = self.value_table[rows]
new_values = (priors * counts + targets) / (counts + 1.0)
with torch.no_grad():
self.value_table[rows] = new_values
self.visit_counts[rows] += 1.0
# ------------------------------------------------------------------
# Endstate-as-target framing (tensor-native, differentiable)
# ------------------------------------------------------------------
def value_toward_endstate(
self,
state_key: str,
is_endstate: bool | Tensor,
*,
terminal_value: float = 1.0,
) -> Tensor:
"""Value expressed as progress toward the merit endstate.
``is_endstate`` true -> ``terminal_value`` (the endstate is the goal, not
a rung); otherwise the learned ``V`` clamped into ``[0, terminal_value]``
so it reads as a fraction of the way to the goal. Differentiable through
``V`` (the clamp is a soft-ish gate via ``torch.clamp``).
"""
if terminal_value < 0.0:
raise ValueError("terminal_value must be non-negative")
raw = self.evaluate(state_key)
is_end = torch.as_tensor(is_endstate, dtype=self.dtype, device=self.device)
terminal = torch.as_tensor(terminal_value, dtype=self.dtype, device=self.device)
clamped = torch.clamp(raw, min=0.0, max=terminal_value) if terminal_value > 0.0 else torch.zeros_like(raw)
return torch.where(is_end > 0, terminal, clamped)
def goal_progress(
self,
state_key: str,
*,
is_endstate: bool | Tensor = False,
) -> Tensor:
"""Shorthand for ``value_toward_endstate(..., terminal_value=1.0)``."""
return self.value_toward_endstate(state_key, is_endstate, terminal_value=1.0)
__all__ = [
"DEFAULT_TABLE_SIZE",
"DEFAULT_TD_ALPHA",
"DEFAULT_VALUE_DISCOUNT",
"TensorValueFunction",
"VALUE_FUNCTION_TENSOR_SCHEMA",
"discounted_returns",
]
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