File size: 6,211 Bytes
d3a24e0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 | """Deduplicated replay buffer for offline + online training.
Stores transitions with frame-level deduplication (hash-based) to
avoid wasting training capacity on near-identical states. Supports
random sampling, prioritized sampling, and buffer persistence.
"""
from __future__ import annotations
import hashlib
import logging
from collections import deque
from dataclasses import dataclass, field
from typing import Iterator
import numpy as np
logger = logging.getLogger(__name__)
@dataclass
class BufferedTransition:
"""A transition stored in the replay buffer.
Attributes:
frame: 64×64 uint8 frame before the action.
action: Action name string.
action_data: Optional action data (e.g. coordinates).
next_frame: 64×64 uint8 frame after the action.
reward: Extrinsic reward (score delta).
frame_changed: Whether the frame visually changed.
frame_hash: MD5 hash of the frame (for dedup).
"""
frame: np.ndarray
action: str
action_data: dict | None
next_frame: np.ndarray
reward: float
frame_changed: bool
frame_hash: str = ""
def __post_init__(self) -> None:
if not self.frame_hash:
self.frame_hash = hashlib.md5(self.frame.tobytes()).hexdigest()
class ReplayBuffer:
"""Deduplicated replay buffer for transition storage.
Features:
- Frame-level deduplication (stores unique frames only once).
- Transition-level deduplication (same state+action → skip).
- Random and prioritized sampling.
- Configurable maximum size.
Attributes:
max_size: Maximum number of transitions.
frames: Dict mapping frame_hash → frame array (deduplicated storage).
transitions: Deque of BufferedTransition objects.
"""
def __init__(self, max_size: int = 200_000) -> None:
"""Initialize the replay buffer.
Args:
max_size: Maximum number of transitions to store.
"""
self.max_size = max_size
self.frames: dict[str, np.ndarray] = {}
self.transitions: deque[BufferedTransition] = deque(maxlen=max_size)
self._seen_keys: set[str] = set()
logger.info("ReplayBuffer initialized (max_size=%d)", max_size)
def add(
self,
frame: np.ndarray,
action: str,
action_data: dict | None,
next_frame: np.ndarray,
reward: float,
frame_changed: bool,
) -> bool:
"""Add a transition to the buffer.
Args:
frame: Frame before action.
action: Action name.
action_data: Optional action data.
next_frame: Frame after action.
reward: Extrinsic reward.
frame_changed: Whether frame visually changed.
Returns:
True if the transition was added, False if deduplicated.
"""
frame_hash = hashlib.md5(frame.tobytes()).hexdigest()
next_hash = hashlib.md5(next_frame.tobytes()).hexdigest()
dedup_key = f"{frame_hash}:{action}:{action_data}"
if dedup_key in self._seen_keys:
return False
self._seen_keys.add(dedup_key)
# Store unique frames
if frame_hash not in self.frames:
self.frames[frame_hash] = frame.copy()
if next_hash not in self.frames:
self.frames[next_hash] = next_frame.copy()
self.transitions.append(
BufferedTransition(
frame=frame.copy(),
action=action,
action_data=action_data,
next_frame=next_frame.copy(),
reward=reward,
frame_changed=frame_changed,
frame_hash=frame_hash,
)
)
return True
def sample(self, batch_size: int) -> list[BufferedTransition]:
"""Sample a random batch of transitions.
Args:
batch_size: Number of transitions to sample.
Returns:
List of BufferedTransition objects.
"""
if len(self.transitions) < batch_size:
return list(self.transitions)
indices = np.random.choice(len(self.transitions), size=batch_size, replace=False)
return [self.transitions[i] for i in indices]
def sample_prioritized(
self,
batch_size: int,
alpha: float = 0.6,
) -> list[BufferedTransition]:
"""Sample a batch with prioritization toward changed frames.
Prioritizes transitions where the frame changed (more informative
for training the world model).
Args:
batch_size: Number of transitions.
alpha: Prioritization exponent (0=uniform, 1=full priority).
Returns:
List of BufferedTransition objects.
"""
if len(self.transitions) < batch_size:
return list(self.transitions)
priorities = np.array([
(1.0 if t.frame_changed else 0.1) ** alpha
for t in self.transitions
])
probs = priorities / priorities.sum()
indices = np.random.choice(len(self.transitions), size=batch_size, p=probs, replace=False)
return [self.transitions[i] for i in indices]
def __len__(self) -> int:
"""Return the number of transitions in the buffer."""
return len(self.transitions)
def __iter__(self) -> Iterator[BufferedTransition]:
"""Iterate over all transitions."""
return iter(self.transitions)
@property
def num_unique_frames(self) -> int:
"""Number of unique frames stored."""
return len(self.frames)
def stats(self) -> dict:
"""Return buffer statistics.
Returns:
Dict with count, unique_frames, changed_ratio.
"""
changed_count = sum(1 for t in self.transitions if t.frame_changed)
return {
"count": len(self.transitions),
"unique_frames": len(self.frames),
"changed_ratio": changed_count / max(len(self.transitions), 1),
}
def clear(self) -> None:
"""Clear all stored data."""
self.frames.clear()
self.transitions.clear()
self._seen_keys.clear()
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