update

README.md

@@ -68,10 +68,22 @@ MCTSNode(
     isterminal::Bool,
     parent::Union{MCTSNode, Nothing},
     children::Dict{String, MCTSNode},
-    etc::Dict{String, Any}
+    etc::Dict{Symbol, Any}
 )
 ```
 
+**Fields:**
+- `nodekey::String` — Unique identifier for the node
+- `state::Dict` — Current state represented as a dictionary
+- `visits::Integer` — Number of times this node has been visited
+- `progressvalue::Number` — LLM's estimate of state quality
+- `statevalue::Number` — Average cumulative reward from simulations
+- `reward::Number` — Immediate reward at this node
+- `isterminal::Bool` — Whether this node represents a terminal state
+- `parent::Union{MCTSNode, Nothing}` — Parent node reference (nothing for root)
+- `children::Dict{String, MCTSNode}` — Mapping of child nodes
+- `etc::Dict{Symbol, Any}` — Additional arbitrary data storage (uses Symbol keys)
+
 ### Understanding `progressvalue`, `statevalue`, and `reward`
 
 | Field | Source | Purpose |
@@ -212,11 +224,33 @@ Run simulation from a node and backpropagate the reward. Returns `nothing`.
 - `multithread::Bool=false` — Enable multithreading
 - `highValueState` — Channel to store high-value states
 
+#### `backpropagate(node, simTrajectoryReward; kwargs...)`
+
+Backpropagate reward along the simulation chain. Updates visit counts and state values for all nodes along the path to the root. Returns `nothing`.
+
+**Arguments:**
+- `node::MCTSNode` — The leaf node from which to start backpropagation
+- `simTrajectoryReward::Number` — The total reward from the trajectory simulation
+
+**Keyword Arguments:**
+- `discountRewardCoeff::AbstractFloat=0.9` — Discount coefficient applied to future rewards
+
 ### Utility Functions
 
 - `UCTselect(node, w)` — Select node using UCT score
 - `dictify(x; keytype=Any)` — Convert JSON.Object/OrderedDict to plain Dict
 
+### MCTS Utility Functions
+
+- `selectBestNextNode(node)` — Select best child node based on value metric
+- `selectBestTrajectoryNode(node)` — Select best node along optimal trajectory
+- `backpropagate(node, simTrajectoryReward; kwargs...)` — Backpropagate reward up the tree
+- `isleaf(node)` — Check if node is a leaf (has no children)
+- `isroot(node)` — Check if node is the root node
+- `selectChildNode(node)` — Select child with highest `progressvalue + reward`
+- `expand(node, transition, transitionargs; kwargs...)` — Generate child nodes
+- `simulate(node, transition, transitionargs; kwargs...)` — Perform rollout simulation
+
 ### MCTS Node Structure
 
 ```julia
@@ -230,7 +264,7 @@ MCTSNode(
     isterminal::Bool,
     parent::Union{MCTSNode, Nothing},
     children::Dict{String, MCTSNode},
-    etc::Dict{String, Any}
+    etc::Dict{Symbol, Any}
 )
 ```