memrl 0.1.2

Memory-augmented reinforcement learning for Claude Code - persistent memory that learns from experience
# MemRL Configuration
# Default settings for the memory system

[capture]
# Auto-capture sessions
auto_capture = true
# Use LLM for intent extraction
extract_intent_llm = true
# Capture git diffs with episodes
capture_diffs = true

[embedding]
# Embedding model (Phase 2)
model = "bge-small-en-v1.5"
# Batch size for embedding generation
batch_size = 32

[retrieval]
# Default number of episodes to retrieve
default_limit = 3
# Weight for semantic similarity (1 - utility_weight)
similarity_weight = 0.3
# Weight for utility score
utility_weight = 0.7
# Minimum similarity threshold
min_similarity = 0.5

[bellman]
# Discount factor for Bellman updates
gamma = 0.9
# Learning rate for utility updates
alpha = 0.1
# How often to run propagation
propagate_interval = "daily"

[storage]
# Maximum age for episodes (days)
max_age_days = 180
# Minimum utility score to keep
min_utility_threshold = 0.05
# Minimum retrievals to consider for pruning
min_retrievals = 2