Returns a path (a list of actions) to the closest dot, starting from gameState.

Generates a new configuration reached by translating the current configuration by the action vector. This is a low-level call and do

Generates a new configuration reached by translating the current configuration by the action vector. This is a low-level call and do

Generates a new configuration reached by translating the current configuration by the action vector. This is a low-level call and do

Updates beliefs, then chooses an action based on updated beliefs.

Returns the cost of a particular sequence of actions. If those actions include an illegal move, return 999999. This is implemented

Returns a noisy distance to each ghost.

Returns an AgentState object for pacman (in game.py) state.pos gives the current position state.direction gives the travel v

Returns an AgentState object for pacman (in game.py) state.pos gives the current position state.direction gives the travel v

Returns an AgentState object for pacman (in game.py) state.pos gives the current position state.direction gives the travel v

NOTE: 'random' is a special policy value; don't use it in your code.

Should return Q(state,action) = w * featureVector where * is the dotProduct operator

Get reward for state, action, nextState transition. Note that the reward depends only on the state being departed (as in the

Returns the start state (in your state space, not the full Pacman state space)

Returns a Grid of boolean wall indicator variables. Grids can be accessed via list notation, so to check if there is a wall

Computes the loss for a batch of examples. The correct labels `y` are represented as a node with shape (batch_size x 10). Ea

Computes the loss for a batch of examples. The correct labels `y` are represented as a node with shape (batch_size x 5). Eac

Calculates the predicted class for a single data point `x`. Returns: 1 or -1

Creates an initial game state from a layout array (see layout.py).

Creates an initial game state from a layout array (see layout.py).

Initialize particles to be consistent with a uniform prior. Particles should be evenly distributed across positions in order to ensur

Returns a list of legal moves from the current state. Moves consist of moving the blank space up, down, left or right. The