# Tiger¶

This is a classic POMDP problem, introduced in . The description of the tiger problem is as follows: (Quote from POMDP: Introduction to Partially Observable Markov Decision Processes by Kamalzadeh and Hahsler ):

A tiger is put with equal probability behind one of two doors, while treasure is put behind the other one. You are standing in front of the two closed doors and need to decide which one to open. If you open the door with the tiger, you will get hurt (negative reward). But if you open the door with treasure, you receive a positive reward. Instead of opening a door right away, you also have the option to wait and listen for tiger noises. But listening is neither free nor entirely accurate. You might hear the tiger behind the left door while it is actually behind the right door and vice versa.

Tiger is a simple POMDP with only 2 states, 2 actions, and 2 observations. In pomdp_py, to define and solve a POMDP:

Note

For a simple POMDP like Tiger, it is encouraged to place the code for all components (e.g. state, action, observation and models) under the same Python module (i.e. the same .py file).

## Define the domain¶

We start by defining the domain ($$S, A, O$$). In pomdp_py, this is equivalent as defining three classes that inherit State, Action, Observation (see basics).

class State(pomdp_py.State):
def __init__(self, name):
if name != "tiger-left" and name != "tiger-right":
raise ValueError("Invalid state: %s" % name)
self.name = name
# ... __hash__, __eq__ should be implemented

class Action(pomdp_py.Action):
def __init__(self, name):
if name != "open-left" and name != "open-right"\
and name != "listen":
raise ValueError("Invalid action: %s" % name)
self.name = name
# ... __hash__, __eq__ should be implemented

class Observation(pomdp_py.Observation):
def __init__(self, name):
if name != "tiger-left" and name != "tiger-right":
raise ValueError("Invalid action: %s" % name)
self.name = name
# ... __hash__, __eq__ should be implemented


[source]

## Define the models¶

Next, we define the models ($$T, O, R, \pi$$). In pomdp_py, this is equivalent as defining classes that inherit ObservationModel, TransitionModel, RewardModel, PolicyModel (see basics).

Note

pomdp_py also provides an interface for BlackboxModel.

We begin with the ObservationModel. In Tiger, when the agent takes the listen action, it observes which side the tiger is with some noise. Implementing such a model in pomdp_py boils down to implementing a generative model with an optional probability function that you can implement when, for example, you need to perform exact belief update. One way of implementing this is as follows. Note that our model inherits the pomdp_py’s ObservationModel interface.

class ObservationModel(pomdp_py.ObservationModel):
def __init__(self, noise=0.15):
self.noise = noise

def probability(self, observation, next_state, action):
if action.name == "listen":
# heard the correct growl
if observation.name == next_state.name:
return 1.0 - self.noise
else:
return self.noise
else:
return 0.5

def sample(self, next_state, action):
if action.name == "listen":
thresh = 1.0 - self.noise
else:
thresh = 0.5

if random.uniform(0,1) < thresh:
return Observation(next_state.name)
else:
return Observation(next_state.other().name)

def get_all_observations(self):
"""Only need to implement this if you're using
a solver that needs to enumerate over the observation
space (e.g. value iteration)"""
return [Observation(s)
for s in {"tiger-left", "tiger-right"}]


[source]

The TransitionModel is deterministic. Similarly, we implement the sample and probability functions in the interface for this generative model:

class TransitionModel(pomdp_py.TransitionModel):
def probability(self, next_state, state, action):
"""According to problem spec, the world resets once
action is open-left/open-right. Otherwise, it
stays the same"""
if action.name.startswith("open"):
return 0.5
else:
if next_state.name == state.name:
return 1.0 - 1e-9
else:
return 1e-9

def sample(self, state, action):
if action.name.startswith("open"):
return random.choice(self.get_all_states())
else:
return State(state.name)

def get_all_states(self):
"""Only need to implement this if you're using
a solver that needs to enumerate over the
observation space (e.g. value iteration)"""
return [State(s) for s in {"tiger-left", "tiger-right"}]


[source]

Since the Tiger domain is small, the transition and observation probabilities can be easily specified by a table (a dictionary in Python), which is similar to specifying POMDPs using POMDP file formats. However, pomdp_py allows more flexible way of implementing these models which can be intractable to enumerate (e.g. continuous).

Next, we define the PolicyModel. The job of a PolicyModel is to (1) determine the set of actions that the robot can take at given state (and/or history); (2) sample an action from this set according to some probability distribution. This allows extensions to policy models that have a prior over actions. The idea of preference over actions have been used in several existing work   . Without prior knowledge of action preference, the PolicyModel can simply sample actions from the set uniformly. Typically, we would like to start without (usually human-engineered) prior knowledge over actions, because it sort of guides the planner and we are not sure if this guidance based on heuristics is actually optimal. So caution must be used.

In the Tiger problem, we just define a simple PolicyModel as follows. We choose not to implement the probability and argmax functions because we don’t really use them for planning; The PolicyModel in this case can do (1) and (2) without those two functions. But in general, the PolicyModel could be learned, or the action space is large so a probability distribution over it becomes important.

class PolicyModel(pomdp_py.RolloutPolicy):
"""A simple policy model with uniform prior over a
small, finite action space"""
ACTIONS = {Action(s)
for s in {"open-left", "open-right", "listen"}}

def sample(self, state):
return random.sample(self.get_all_actions(), 1)

def rollout(self, state, *args):
"""Treating this PolicyModel as a rollout policy"""
return self.sample(state)

def get_all_actions(self, state=None, history=None):
return PolicyModel.ACTIONS


[source]

Note that the sample function is not used directly during planning with POMCP or POUCT; Instead, the rollout policy’s sampling process is defined through the rollout function; In the example here, indeed, you could explicitly say that the rollout sampling is just sampling from this policy model through the sample function.

Note

The original POMCP/POUCT paper  provides a way to inject problem-specific action prior to POMDP planning; pomdp_py allows the user to do this through defining ActionPrior. See Preference-based Action Prior for details.

Note

Also, regarding rollout, you can implement the rollout policy as $$\pi(a|h)$$ by defining that function as:

def rollout(self, state, history)


and you would have access to a partial history that contains the [(action, observation), ...] sequence starting from the first step of the online search tree created when using POMCP/POUCT.

Finally, we define the RewardModel. It is straightforward according to the problem description. In this case, (and very commonly), the reward function is deterministic. We can implement it as follows. The interface for reward model does allow stochastic rewards.

class RewardModel(pomdp_py.RewardModel):
def _reward_func(self, state, action):
if action.name == "open-left":
if state.name == "tiger-right":
return 10
else:
return -100
elif action.name == "open-right":
if state.name == "tiger-left":
return 10
else:
return -100
else: # listen
return -1

def sample(self, state, action, next_state):
# deterministic
return self._reward_func(state, action)


[source]

## Define the POMDP¶

With the models that we have defined, it is simple to define a POMDP for the Tiger problem; To do this, we need to define Agent, and Environment. Note that you could just construct an agent and an environment and still be able to plan actions and simulate the environment. This class is mostly just for code organization and is entirely optional.

class TigerProblem(pomdp_py.POMDP):

def __init__(self, obs_noise, init_true_state, init_belief):
"""init_belief is a Distribution."""
agent = pomdp_py.Agent(init_belief,
PolicyModel(),
TransitionModel(),
ObservationModel(obs_noise),
RewardModel())
env = pomdp_py.Environment(init_true_state,
TransitionModel(),
RewardModel())
super().__init__(agent, env, name="TigerProblem")


[source]

Notice that init_true_state and init_belief need to be provided. The process of creating them is described in more detail in the next section.

Note

It is entirely optional to define a Problem class (like TigerProblem) that extends the pomdp_py.framework.basics.POMDP class in order to use a pomdp_py.framework.planner.Planner to solve a POMDP; Only the Agent and the Environment are needed. The POMDP class sometimes can organize the parameters that need to be passed into the constructors of Agent and Environment. For complicated problems, specific Agent and Environment classes are written that inherit pomdp_py.framework.basics.Agent and pomdp_py.framework.basics.Environment.

## Instantiate the POMDP¶

Now we have a definition of the Tiger problem. Now, we need to instantiate a problem by providing parameters for the models, the initial state of the environment, and the initial belief of the agent.

In Tiger, the model parameters are basically the probabilities for $$T$$ and $$O$$, which have been described above (see Define the models).

We can create a random initial state and a uniform belief as follows:

init_true_state = random.choice([State("tiger-left"),
State("tiger-right")])
init_belief = pomdp_py.Histogram({State("tiger-left"): 0.5,
State("tiger-right"): 0.5})


Then, we can create an instance of the Tiger problem with the standard noise of 0.15:

tiger_problem = TigerProblem(0.15, init_true_state, init_belief)


[source]

## Solve the POMDP instance¶

To solve a POMDP with pomdp_py, here are the basic steps:

1. Create a planner (Planner)

2. Agent plans an action $$a_t$$.

3. Environment state transitions $$s_t \rightarrow s_{t+1}$$ according to its transition model. Reward $$r_t$$ is returned as a result of the transition.

4. Agent receives an observation $$o_t$$.

5. Agent updates history and belief $$h_t,b_t \rightarrow h_{t+1},b_{t+1}$$ where $$h_{t+1} = h_t \cup (a_t, o_t)$$.

• This could be done either by updating the belief of an agent directly, or through an update of the planner. More specifically, if the planner is POMCP, updating the planner will result in the agent belief update as well. But for POUCT or ValueIteration, the agent belief needs to be updated explicitly.

6. Unless termination condition is reached, repeat steps 2-6.

For the Tiger problem, we implemented this procedure as follows:

# Step 1; in main()
# creating planners
vi = pomdp_py.ValueIteration(horizon=3, discount_factor=0.95)
pouct = pomdp_py.POUCT(max_depth=3, discount_factor=0.95,
planning_time=.5, exploration_const=110,
rollout_policy=tiger_problem.agent.policy_model)
pomcp = pomdp_py.POMCP(max_depth=3, discount_factor=0.95,
planning_time=.5, exploration_const=110,
rollout_policy=tiger_problem.agent.policy_model)
...  # call test_planner() for steps 2-6.

# Steps 2-6; called in main()
def test_planner(tiger_problem, planner, nsteps=3):
"""Runs the action-feedback loop of Tiger problem POMDP"""
for i in range(nsteps):  # Step 6
# Step 2
action = planner.plan(tiger_problem.agent)

print("==== Step %d ====" % (i+1))
print("True state:", tiger_problem.env.state)
print("Belief:", tiger_problem.agent.cur_belief)
print("Action:", action)
# Step 3; There is no state transition for the tiger domain.
# In general, the ennvironment state can be transitioned
# using
#
#   reward = tiger_problem.env.state_transition(action, execute=True)
#
# Or, it is possible that you don't have control
# over the environment change (e.g. robot acting
# in real world); In that case, you could skip
# the state transition and re-estimate the state
# (e.g. through the perception stack on the robot).
reward = tiger_problem.env.reward_model.sample(tiger_problem.env.state, action, None)
print("Reward:", reward)

# Step 4
# Let's create some simulated real observation;
# Here, we use observation based on true state for sanity
# checking solver behavior. In general, this observation
# should be sampled from agent's observation model, as
#
#    real_observation = tiger_problem.agent.observation_model.sample(tiger_problem.env.state, action)
#
# or coming from an external source (e.g. robot sensor
# reading). Note that tiger_problem.env.state should store
# the environment state after transition.
real_observation = Observation(tiger_problem.env.state.name)
print(">> Observation: %s" % real_observation)

# Step 5
# Update the belief. If the planner is POMCP, planner.update
# also automatically updates agent belief.
tiger_problem.agent.update_history(action, real_observation)
planner.update(tiger_problem.agent, action, real_observation)
if isinstance(planner, pomdp_py.POUCT):
print("Num sims: %d" % planner.last_num_sims)
if isinstance(tiger_problem.agent.cur_belief, pomdp_py.Histogram):
new_belief = pomdp_py.update_histogram_belief(tiger_problem.agent.cur_belief,
action, real_observation,
tiger_problem.agent.observation_model,
tiger_problem.agent.transition_model)
tiger_problem.agent.set_belief(new_belief)


[source]

## Summary¶

In short, to use pomdp_py to define a POMDP problem and solve an instance of the problem,

1

Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99–134, 1998.

2(1,2)

David Silver and Joel Veness. Monte-carlo planning in large pomdps. In Advances in neural information processing systems, 2164–2172. 2010.

3

David Abel, David Ellis Hershkowitz, Gabriel Barth-Maron, Stephen Brawner, Kevin O'Farrell, James MacGlashan, and Stefanie Tellex. Goal-based action priors. In Twenty-Fifth International Conference on Automated Planning and Scheduling. 2015.

4

Yuchen Xiao, Sammie Katt, Andreas ten Pas, Shengjian Chen, and Christopher Amato. Online planning for target object search in clutter under partial observability. In Proceedings of the International Conference on Robotics and Automation. 2019.