| computePolicy | Computes the reinforcement learning policy | 
| epsilonGreedyActionSelection | Performs \varepsilon-greedy action selection | 
| experienceReplay | Performs experience replay | 
| gridworldEnvironment | Defines an environment for a gridworld example | 
| lookupActionSelection | Converts a name into an action selection function | 
| lookupLearningRule | Loads reinforcement learning algorithm | 
| policy | Computes the reinforcement learning policy | 
| randomActionSelection | Performs random action selection | 
| ReinforcementLearning | Performs reinforcement learning | 
| replayExperience | Performs experience replay | 
| rl | Performs reinforcement learning | 
| sampleExperience | Sample state transitions from an environment function | 
| sampleGridSequence | Sample grid sequence | 
| selectEpsilonGreedyAction | Performs \varepsilon-greedy action selection | 
| selectRandomAction | Performs random action selection | 
| state | Creates a state representation for arbitrary objects | 
| tictactoe | Game states of 100,000 randomly sampled Tic-Tac-Toe games. |