View source: R/simulate_POMDP.R
simulate_POMDP | R Documentation |
Simulate trajectories through a POMDP. The start state for each trajectory is randomly chosen using the specified belief. The belief is used to choose actions from the the epsilon-greedy policy and then updated using observations.
simulate_POMDP(
model,
n = 100,
belief = NULL,
horizon = NULL,
return_beliefs = FALSE,
epsilon = NULL,
digits = 7,
engine = "cpp",
verbose = FALSE,
...
)
model |
a POMDP model. |
n |
number of trajectories. |
belief |
probability distribution over the states for choosing the starting states for the trajectories. Defaults to the start belief state specified in the model or "uniform". |
horizon |
number of epochs for the simulation. If |
return_beliefs |
logical; Return all visited belief states? This requires n x horizon memory. |
epsilon |
the probability of random actions for using an epsilon-greedy policy. Default for solved models is 0 and for unsolved model 1. |
digits |
round probabilities for belief points. |
engine |
|
verbose |
report used parameters. |
... |
further arguments are ignored. |
A native R implementation is available (engine = 'r'
) and a faster C++ implementation
(engine = 'cpp'
).
Both implementations support parallel execution using the package
foreach. To enable parallel execution, a parallel backend like
doparallel needs to be available needs to be registered (see
doParallel::registerDoParallel()
).
Note that small simulations are slower using parallelization. Therefore, C++ simulations
with n * horizon less than 100,000 are always executed using a single worker.
A list with elements:
avg_reward
: The average discounted reward.
belief_states
: A matrix with belief states as rows.
action_cnt
: Action counts.
state_cnt
: State counts.
reward
: Reward for each trajectory.
Michael Hahsler
Other POMDP:
POMDP_accessors
,
POMDP()
,
plot_belief_space()
,
projection()
,
regret()
,
sample_belief_space()
,
solve_POMDP()
,
solve_SARSOP()
,
transition_graph()
,
update_belief()
,
value_function()
,
write_POMDP()
data(Tiger)
# solve the POMDP for 5 epochs and no discounting
sol <- solve_POMDP(Tiger, horizon = 5, discount = 1, method = "enum")
sol
policy(sol)
# uncomment the following line to register a parallel backend for simulation
# (needs package doparallel installed)
# doParallel::registerDoParallel()
## Example 1: simulate 10 trajectories
sim <- simulate_POMDP(sol, n = 100, verbose = TRUE)
sim
# calculate the percentage that each action is used in the simulation
round_stochastic(sim$action_cnt / sum(sim$action_cnt), 2)
# reward distribution
hist(sim$reward)
## Example 2: look at all belief states in the trajectory starting with an initial start belief.
sim <- simulate_POMDP(sol, n = 100, belief = c(.5, .5), return_beliefs = TRUE)
head(sim$belief_states)
# plot with added density (the x-axis is the probability of the second belief state)
plot_belief_space(sol, sample = sim$belief_states, jitter = 2, ylim = c(0, 6))
lines(density(sim$belief_states[, 2], bw = .02)); axis(2); title(ylab = "Density")
## Example 3: simulate trajectories for an unsolved POMDP which uses an epsilon of 1
# (i.e., all actions are randomized)
sim <- simulate_POMDP(Tiger, n = 100, horizon = 5, return_beliefs = TRUE, verbose = TRUE)
sim$avg_reward
plot_belief_space(sol, sample = sim$belief_states, jitter = 2, ylim = c(0, 6))
lines(density(sim$belief_states[, 1], bw = .05)); axis(2); title(ylab = "Density")
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