Windy_gridworld: Windy Gridworld MDP

In pomdp: Infrastructure for Partially Observable Markov Decision Processes (POMDP)

Windy Gridworld MDP

Description

The Windy gridworld MDP example from Chapter 6 of the textbook "Reinforcement Learning: An Introduction."

Format

An object of class MDP.

Details

The gridworld has the following layout:

The grid world is represented as a 7 x 10 matrix of states. In the middle region the next states are shifted upward by wind (the strength in number of squares is given below each column). For example, if the agent is one cell to the right of the goal, then the action left takes the agent to the cell just above the goal.

No discounting is used (i.e., \gamma = 1).

References

Richard S. Sutton and Andrew G. Barto (2018). Reinforcement Learning: An Introduction Second Edition, MIT Press, Cambridge, MA.

See Also

Other MDP_examples: Cliff_walking, MDP(), Maze

Other gridworld: Cliff_walking, Maze, gridworld

Examples

data(Windy_gridworld)
Windy_gridworld

gridworld_matrix(Windy_gridworld)
gridworld_matrix(Windy_gridworld, what = "labels")

# The Goal is an absorbing state 
which(absorbing_states(Windy_gridworld))

# visualize the transition graph
gridworld_plot_transition_graph(Windy_gridworld, 
  vertex.size = 10, vertex.label = NA)

# solve using value iteration
sol <- solve_MDP(Windy_gridworld) 
sol
policy(sol)
gridworld_plot_policy(sol)

pomdp documentation built on May 29, 2024, 2:04 a.m.