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+# Decision Making Approaches
+
+Responsible for making decisions based on information provided by other system components. It should account for all possible traffic scenarios to ensure comprehensive coverage and safe driving behavior
+
+## Decision Tree - current implementations
+
+- a tool to organizes decisions and their possible outcomes in a tree-like structure
+- a branch represents a choice/condition which may lead to further options.
+  - starting at the root: checking surrounding conditions (traffic lights, obstacles, speed limits etc) branching out based on yes/no questions at each node
+- in our case each node is deterministic (as no ML is used)
+
+### Pros and Cons
+
+- `+` are easy to understand and interpret
+- `+` decision paths are traceable → suitable for debugging
+- `+` deterministic outputs beneficial when predictability is essential
+- `+` compared to NN, low computational cost
+- `-` Adding too many branches for every possible situation can make the tree unwieldy and hard to manage
+  - can easily overfit with too many conditions
+- `-` no reasoning and no probabilistic capabilities; trees tend to make suboptimal decisions in new, unclear situations
+  - hard to account for every situation
+
+### Changes in Previous Semesters
+
+- removing redundant behaviours
+
+  - reducing to `Intersection`, `Lane Switch` and `Cruise`
+
+![decision_tree](/doc/assets/behaviour_tree.png)
+
+## Finite State Machine - previous implementations
+
+- model with an discrete number of possible states with rule-based control
+  - can only inherit one state at a time
+- each state represents a specific driving mode, such as "Lane Switching" "Stopping," or "Overtaking"
+  - triggered by inputs like traffic signals, obstacles, or speed limits
+
+### Pros and Cons
+
+- `+` each state and transition is explicitly defined → suitable for debugging and testing
+- `+` easier implementations than a decision tree
+- `-` scalability issues with increasing number of states
+- `-` less flexible than decision trees
+
+## Combining Planning and Reinforcement Learning
+
+[C.-J. Hoel, K. Driggs-Campbell, K. Wolff, L. Laine, and M. J. Kochenderfer, "Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Driving," arXiv preprint arXiv:1905.02680, 2019.](https://arxiv.org/abs/1905.02680)
+
+- Monte Carlo Tree Search (MCTS): Used for planning by creating a search tree that simulates possible actions, selecting those that maximize expected rewards (maintaining desired speed, reaching end points, avoinding collision etc.)
+  - considers all possible actions → Each action creates a new branch that represents what the new situation might look like if the agent took that action
+- deep neural network guides the MCTS by predicting action probabilities (which actions are most likely to lead to good outcomes: changing lanes, speeding up, breaking etc.) and state values (scores that tell the agent whether its current situation is favorable or not based on expected future rewards)
+
+### Pros and Cons
+
+- `+` generalizes better to complex scenarios
+  - takes advantage of continuous and high-dimensional state spaces
+- `-` computational complexity and real-time feasibility
+- `-` extensive training data and tuning required to handle a broad range of driving situations