Man pages for nproellochs/ReinforcementLearning
Model-Free Reinforcement Learning
| 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 |
| 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. |
