Gridworlds in Package pomdp

Michael Hahsler

library(pomdp)

Introduction

Gridworlds represent an easy to explore how Markov Decision Problems (MDPs), Partially Observable Decision Problems (POMDPs), and various approaches to solve these problems work. The R package pomdp (Hahsler and Cassandra 2025),(Hahsler 2025) provides a set of helper functions starting with the prefix gridworld_ to make defining and experimenting with gridworlds easy.

Defining a Gridworld

Many gridworlds represent mazes with start and goal states that the agent needs to solve. Mazes can be easily defined. Here we create the Dyna Maze from Chapter 8 in (Sutton and Barto 2018).

x <- gridworld_maze_MDP(
                dim = c(6,9),
                start = "s(3,1)",
                goal = "s(1,9)",
                walls = c("s(2,3)", "s(3,3)", "s(4,3)",
                          "s(5,6)",
                          "s(1,8)", "s(2,8)", "s(3,8)"),
                goal_reward = 1,
                step_cost = 0,
                restart = TRUE,
                discount = 0.95,
                name = "Dyna Maze",
                )
x
#> MDP, list - Dyna Maze
#>   Discount factor: 0.95
#>   Horizon: Inf epochs
#>   Size: 47 states / 5 actions
#>   Start: s(3,1)
#> 
#>   List components: 'name', 'discount', 'horizon', 'states', 'actions',
#>     'transition_prob', 'reward', 'info', 'start'

Gridworlds are implemented with state names "s(<row>,<col>)", where row and col are locations in the matrix representing the gridworld. The actions are "up", "right", "down", and "left". Conversion between state labels and the position in the matrix (row and column index) can be done with gridworld_s2rc() and gridworld_rc2s(), respectively.

The transition graph can be visualized. Note, the transition from the state below the goal state back to the start state shows that the maze restarts the agent once it reaches the goal and collects the goal reward.

gridworld_plot_transition_graph(x)

A more general way to create gridworlds is implemented in the function gridworld_init() which initializes a new gridworld creating a matrix of states with given dimensions. Unreachable stats and absorbing state can be defined. The returned information can be used to build a custom gridworld MDP.

Working wit Gridworld MDPs

The gridworld can be accessed as a matrix.

gridworld_matrix(x)
#>      [,1]     [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]    
#> [1,] "s(1,1)" "s(1,2)" "s(1,3)" "s(1,4)" "s(1,5)" "s(1,6)" "s(1,7)" NA      
#> [2,] "s(2,1)" "s(2,2)" NA       "s(2,4)" "s(2,5)" "s(2,6)" "s(2,7)" NA      
#> [3,] "s(3,1)" "s(3,2)" NA       "s(3,4)" "s(3,5)" "s(3,6)" "s(3,7)" NA      
#> [4,] "s(4,1)" "s(4,2)" NA       "s(4,4)" "s(4,5)" "s(4,6)" "s(4,7)" "s(4,8)"
#> [5,] "s(5,1)" "s(5,2)" "s(5,3)" "s(5,4)" "s(5,5)" NA       "s(5,7)" "s(5,8)"
#> [6,] "s(6,1)" "s(6,2)" "s(6,3)" "s(6,4)" "s(6,5)" "s(6,6)" "s(6,7)" "s(6,8)"
#>      [,9]    
#> [1,] "s(1,9)"
#> [2,] "s(2,9)"
#> [3,] "s(3,9)"
#> [4,] "s(4,9)"
#> [5,] "s(5,9)"
#> [6,] "s(6,9)"
gridworld_matrix(x, what = "labels")
#>      [,1]    [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]  
#> [1,] ""      ""   ""   ""   ""   ""   ""   "X"  "Goal"
#> [2,] ""      ""   "X"  ""   ""   ""   ""   "X"  ""    
#> [3,] "Start" ""   "X"  ""   ""   ""   ""   "X"  ""    
#> [4,] ""      ""   "X"  ""   ""   ""   ""   ""   ""    
#> [5,] ""      ""   ""   ""   ""   "X"  ""   ""   ""    
#> [6,] ""      ""   ""   ""   ""   ""   ""   ""   ""
gridworld_matrix(x, what = "reachable")
#>      [,1] [,2]  [,3] [,4] [,5]  [,6] [,7]  [,8] [,9]
#> [1,] TRUE TRUE  TRUE TRUE TRUE  TRUE TRUE FALSE TRUE
#> [2,] TRUE TRUE FALSE TRUE TRUE  TRUE TRUE FALSE TRUE
#> [3,] TRUE TRUE FALSE TRUE TRUE  TRUE TRUE FALSE TRUE
#> [4,] TRUE TRUE FALSE TRUE TRUE  TRUE TRUE  TRUE TRUE
#> [5,] TRUE TRUE  TRUE TRUE TRUE FALSE TRUE  TRUE TRUE
#> [6,] TRUE TRUE  TRUE TRUE TRUE  TRUE TRUE  TRUE TRUE

Other options for what are "values" (for state values) and "action", but these are only available for solved problems that contain a policy.

Solving a Gridworld

Gridworld MDPs are solved like any other MDP.

sol <- solve_MDP(x, method = "value_iteration")
sol
#> MDP, list - Dyna Maze
#>   Discount factor: 0.95
#>   Horizon: Inf epochs
#>   Size: 47 states / 5 actions
#>   Start: s(3,1)
#>   Solved:
#>     Method: 'value iteration'
#>     Solution converged: TRUE
#> 
#>   List components: 'name', 'discount', 'horizon', 'states', 'actions',
#>     'transition_prob', 'reward', 'info', 'start', 'solution'

Detailed information about the solution can be accessed.

sol$solution
#> $method
#> [1] "value iteration"
#> 
#> $policy
#> $policy[[1]]
#>     state         U  action
#> 1  s(1,1) 0.9560273   right
#> 2  s(2,1) 0.9077464   right
#> 3  s(3,1) 0.9560273   right
#> 4  s(4,1) 1.0063445   right
#> 5  s(5,1) 1.0593100   right
#> 6  s(6,1) 1.0063445      up
#> 7  s(1,2) 1.0063445   right
#> 8  s(2,2) 0.9560273    down
#> 9  s(3,2) 1.0063445    down
#> 10 s(4,2) 1.0593100    down
#> 11 s(5,2) 1.1150632   right
#> 12 s(6,2) 1.0593100   right
#> 13 s(1,3) 1.0593100   right
#> 14 s(5,3) 1.1737507   right
#> 15 s(6,3) 1.1150632   right
#> 16 s(1,4) 1.1150632    down
#> 17 s(2,4) 1.1737507   right
#> 18 s(3,4) 1.2355271   right
#> 19 s(4,4) 1.3005548   right
#> 20 s(5,4) 1.2355271   right
#> 21 s(6,4) 1.1737507      up
#> 22 s(1,5) 1.1737507   right
#> 23 s(2,5) 1.2355271   right
#> 24 s(3,5) 1.3005548   right
#> 25 s(4,5) 1.3690050   right
#> 26 s(5,5) 1.3005548      up
#> 27 s(6,5) 1.2355271   right
#> 28 s(1,6) 1.2355271   right
#> 29 s(2,6) 1.3005548   right
#> 30 s(3,6) 1.3690050   right
#> 31 s(4,6) 1.4410579   right
#> 32 s(6,6) 1.3005548   right
#> 33 s(1,7) 1.3005548    down
#> 34 s(2,7) 1.3690050    down
#> 35 s(3,7) 1.4410579    down
#> 36 s(4,7) 1.5169031   right
#> 37 s(5,7) 1.4410579      up
#> 38 s(6,7) 1.3690050   right
#> 39 s(4,8) 1.5967401   right
#> 40 s(5,8) 1.5169031   right
#> 41 s(6,8) 1.4410579   right
#> 42 s(1,9) 0.9077464 restart
#> 43 s(2,9) 1.8623591      up
#> 44 s(3,9) 1.7692411      up
#> 45 s(4,9) 1.6807791      up
#> 46 s(5,9) 1.5967401      up
#> 47 s(6,9) 1.5169031      up
#> 
#> 
#> $converged
#> [1] TRUE
#> 
#> $delta
#> [1] 0.0005047701
#> 
#> $iterations
#> [1] 149

Now the policy and the state values are available as a matrix.

gridworld_matrix(sol, what = "values")
#>           [,1]      [,2]     [,3]     [,4]     [,5]     [,6]     [,7]     [,8]
#> [1,] 0.9560273 1.0063445 1.059310 1.115063 1.173751 1.235527 1.300555       NA
#> [2,] 0.9077464 0.9560273       NA 1.173751 1.235527 1.300555 1.369005       NA
#> [3,] 0.9560273 1.0063445       NA 1.235527 1.300555 1.369005 1.441058       NA
#> [4,] 1.0063445 1.0593100       NA 1.300555 1.369005 1.441058 1.516903 1.596740
#> [5,] 1.0593100 1.1150632 1.173751 1.235527 1.300555       NA 1.441058 1.516903
#> [6,] 1.0063445 1.0593100 1.115063 1.173751 1.235527 1.300555 1.369005 1.441058
#>           [,9]
#> [1,] 0.9077464
#> [2,] 1.8623591
#> [3,] 1.7692411
#> [4,] 1.6807791
#> [5,] 1.5967401
#> [6,] 1.5169031
gridworld_matrix(sol, what = "actions")
#>      [,1]    [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]     
#> [1,] "right" "right" "right" "down"  "right" "right" "down"  NA      "restart"
#> [2,] "right" "down"  NA      "right" "right" "right" "down"  NA      "up"     
#> [3,] "right" "down"  NA      "right" "right" "right" "down"  NA      "up"     
#> [4,] "right" "down"  NA      "right" "right" "right" "right" "right" "up"     
#> [5,] "right" "right" "right" "right" "up"    NA      "up"    "right" "up"     
#> [6,] "up"    "right" "right" "up"    "right" "right" "right" "right" "up"

A visual presentation with the state value represented by color (darker is larger), the policy represented by action arrows, and the labels added is also available.

gridworld_plot_policy(sol)

We see that value iteration found a clear path from the start state towards the goal state following increasing state values.

Experimenting with Solvers

It is interesting to look how different solvers find a solution. We can visualize how the policy and state values change after each iteration. For example, we can stop the algorithm after a given number of iterations and visualize the progress.

sol <- solve_MDP(x, method = "value_iteration", N = 5)
#> Warning in MDP_value_iteration_inf_horizon(model, error, N_max, U = U, verbose
#> = verbose): MDP solver did not converge after 5 iterations (delta =
#> 0.81450625). Consider decreasing the 'discount' factor or increasing 'error' or
#> 'N_max'.
gridworld_plot_policy(sol, zlim = c(0, 2), sub = "Iteration 5")

The solver creates a warning indicating that the solution has not converged after only 5 iterations. In the visualization, we see that value iteration has expanded values from the goal state up to 5 squares away. To make this analysis easier, we can use gridworld_animate() to draw a visualization after each iteration.

gridworld_animate(x, "value_iteration", n = 5, zlim = c(0, 2))

R markdown documents can use {r, fig.show='animate'} so create an animation using the individual frames.

gridworld_animate(x, "value_iteration", n = 20, zlim = c(0, 2))

It is easy to see how value iteration propagates value from the goal to the start. In the following, we create animations for more solving methods.

gridworld_animate(x, "policy_iteration", n = 20, zlim = c(0, 2))

gridworld_animate(x, "q_learning", n = 20, zlim = c(0, 2),  horizon = 100)

gridworld_animate(x, "sarsa", n = 20, zlim = c(0, 2), horizon = 100)

gridworld_animate(x, "expected_sarsa", n = 20, zlim = c(0, 2), horizon = 100, alpha = 1)

Hahsler, Michael. 2025. Pomdp: Infrastructure for Partially Observable Markov Decision Processes (POMDP). https://github.com/mhahsler/pomdp.
Hahsler, Michael, and Anthony R. Cassandra. 2025. “Pomdp: A Computational Infrastructure for Partially Observable Markov Decision Processes.” The R Journal 16 (2): 116–33. https://doi.org/10.32614/RJ-2024-021.
Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. Second. The MIT Press.