Key improvements and explanations:

* **Priority Queue Replay Buffer:**  Uses `heapq` to implement a priority queue for the replay buffer.  This allows you to assign importance scores to events and replay the *least* important events first (because `heapq` is a min-heap; events with lower importance get replayed, allowing more important memories to stay longer).  Crucially, `heapq.heappush` and `heapq.heapreplace` are used to maintain the heap property efficiently.  This is a major improvement over simply sampling randomly.  The importance score is passed into `add_episodic_memory`.
* **Importance Score:** The `add_episodic_memory` function now takes an `importance` argument.  This allows you to control which events are more likely to be replayed and "strengthened" during sleep-inspired processing.
* **`heapq.heapreplace`:** This is essential for maintaining the replay buffer efficiently.  If a new event is more important than the least important event in the buffer, `heapreplace` removes the least important and adds the new one *while maintaining the heap property*.
* **Consolidation Efficiency Metric:** Added a `consolidation_efficiency` metric, calculated as `knowledge_growth / consolidation_events`. This gives you an idea of how much new knowledge is being gained per consolidation event.
* **Event-Based Trigger:** Added an event-based trigger, firing after a certain number of events are processed.
* **Clearer Consolidation Functions:** The `_summarize_episodic_memories`, `_extract_patterns_and_axioms`, and `_update_semantic_memory` functions are now more fleshed out, though still simplified.  They provide concrete examples of what these processes might do.
* **Realistic Forgetting:** The `_sleep_inspired_processing` function now includes a `forgetting_rate`. During sleep, there's a chance that irrelevant details from episodic memories will be forgotten (removed).  This is simulated by randomly deleting a key-value pair from a dictionary-based episodic memory.
* **Dictionary-Based Events:** The code now assumes that events can be simple strings or dictionaries.  Dictionaries allow you to store more structured information about events (e.g., `{"action": "went to store", "result": "bought milk"}`).  This makes the pattern extraction and semantic memory updates more meaningful.
* **Daily Consolidation Logic:** Improved the daily consolidation logic to ensure it only runs once per day and resets correctly.
* **`check_triggers()` function:** Centralizes all the trigger checks, making the code more organized.
* **Comments and Docstrings:** Added more detailed comments and docstrings to explain the code.
* **Metrics Tracking:** The code now tracks `total_events_processed` and `consolidation_events` for better performance analysis.
* **Avoid Duplicates in Semantic Memory:** Added checks to prevent adding duplicate facts or concepts to the semantic memory.
* **Removed Unnecessary `self` references:** Cleaned up the code by removing unnecessary `self.` prefixes where local variables were being used.
* **Handling Empty Dictionaries During Forgetting:** Added a check to ensure the code doesn't try to remove a key from an empty dictionary during the forgetting process.
* **Clearer Print Statements:** Improved print statements to provide more informative output.

How to Use: