Merge branch 'master' into split-fail-focus

predicators/approaches/bilevel_planning_approach.py

@@ -31,7 +31,8 @@ def __init__(self,
                  train_tasks: List[Task],
                  task_planning_heuristic: str = "default",
                  max_skeletons_optimized: int = -1,
-                 bilevel_plan_without_sim: Optional[bool] = None) -> None:
+                 bilevel_plan_without_sim: Optional[bool] = None,
+                 option_model: Optional[_OptionModelBase] = None) -> None:
         super().__init__(initial_predicates, initial_options, types,
                          action_space, train_tasks)
         if task_planning_heuristic == "default":
@@ -43,7 +44,9 @@ def __init__(self,
         self._task_planning_heuristic = task_planning_heuristic
         self._max_skeletons_optimized = max_skeletons_optimized
         self._plan_without_sim = bilevel_plan_without_sim
-        self._option_model = create_option_model(CFG.option_model_name)
+        if option_model is None:
+            option_model = create_option_model(CFG.option_model_name)
+        self._option_model = option_model
         self._num_calls = 0
         self._last_plan: List[_Option] = []  # used if plan WITH sim
         self._last_nsrt_plan: List[_GroundNSRT] = []  # plan WITHOUT sim

predicators/approaches/bridge_policy_approach.py

@@ -51,8 +51,9 @@
 from predicators.approaches.oracle_approach import OracleApproach
 from predicators.bridge_policies import BridgePolicyDone, create_bridge_policy
 from predicators.nsrt_learning.segmentation import segment_trajectory
+from predicators.option_model import _OptionModelBase
 from predicators.settings import CFG
-from predicators.structs import Action, BridgeDataset, DefaultState, \
+from predicators.structs import NSRT, Action, BridgeDataset, DefaultState, \
     DemonstrationQuery, DemonstrationResponse, InteractionRequest, \
     InteractionResult, ParameterizedOption, Predicate, Query, State, Task, \
     Type, _Option
@@ -69,10 +70,18 @@ def __init__(self,
                  action_space: Box,
                  train_tasks: List[Task],
                  task_planning_heuristic: str = "default",
-                 max_skeletons_optimized: int = -1) -> None:
-        super().__init__(initial_predicates, initial_options, types,
-                         action_space, train_tasks, task_planning_heuristic,
-                         max_skeletons_optimized)
+                 max_skeletons_optimized: int = -1,
+                 nsrts: Optional[Set[NSRT]] = None,
+                 option_model: Optional[_OptionModelBase] = None) -> None:
+        super().__init__(initial_predicates,
+                         initial_options,
+                         types,
+                         action_space,
+                         train_tasks,
+                         task_planning_heuristic,
+                         max_skeletons_optimized,
+                         nsrts=nsrts,
+                         option_model=option_model)
         predicates = self._get_current_predicates()
         options = initial_options
         nsrts = self._get_current_nsrts()

predicators/approaches/oracle_approach.py

@@ -13,6 +13,7 @@
 from predicators.approaches.bilevel_planning_approach import \
     BilevelPlanningApproach
 from predicators.ground_truth_models import get_gt_nsrts
+from predicators.option_model import _OptionModelBase
 from predicators.settings import CFG
 from predicators.structs import NSRT, ParameterizedOption, Predicate, Task, \
     Type
@@ -29,12 +30,22 @@ def __init__(self,
                  train_tasks: List[Task],
                  task_planning_heuristic: str = "default",
                  max_skeletons_optimized: int = -1,
-                 bilevel_plan_without_sim: Optional[bool] = None) -> None:
-        super().__init__(initial_predicates, initial_options, types,
-                         action_space, train_tasks, task_planning_heuristic,
-                         max_skeletons_optimized, bilevel_plan_without_sim)
-        self._nsrts = get_gt_nsrts(CFG.env, self._initial_predicates,
-                                   self._initial_options)
+                 bilevel_plan_without_sim: Optional[bool] = None,
+                 nsrts: Optional[Set[NSRT]] = None,
+                 option_model: Optional[_OptionModelBase] = None) -> None:
+        super().__init__(initial_predicates,
+                         initial_options,
+                         types,
+                         action_space,
+                         train_tasks,
+                         task_planning_heuristic,
+                         max_skeletons_optimized,
+                         bilevel_plan_without_sim,
+                         option_model=option_model)
+        if nsrts is None:
+            nsrts = get_gt_nsrts(CFG.env, self._initial_predicates,
+                                 self._initial_options)
+        self._nsrts = nsrts
 
     @classmethod
     def get_name(cls) -> str:

predicators/option_model.py

@@ -7,15 +7,16 @@
 from __future__ import annotations
 
 import abc
-from typing import Tuple
+from typing import Callable, Set, Tuple
 
 import numpy as np
 
 from predicators import utils
-from predicators.envs import BaseEnv, create_new_env
+from predicators.envs import create_new_env
 from predicators.ground_truth_models import get_gt_options
 from predicators.settings import CFG
-from predicators.structs import DefaultState, State, _Option
+from predicators.structs import Action, DefaultState, ParameterizedOption, \
+    State, _Option
 
 
 def create_option_model(name: str) -> _OptionModelBase:
@@ -24,13 +25,15 @@ def create_option_model(name: str) -> _OptionModelBase:
         env = create_new_env(CFG.env,
                              do_cache=False,
                              use_gui=CFG.option_model_use_gui)
-        return _OracleOptionModel(env)
+        options = get_gt_options(env.get_name())
+        return _OracleOptionModel(options, env.simulate)
     if name.startswith("oracle"):
         env_name = name[name.index("_") + 1:]
         env = create_new_env(env_name,
                              do_cache=False,
                              use_gui=CFG.option_model_use_gui)
-        return _OracleOptionModel(env)
+        options = get_gt_options(env.get_name())
+        return _OracleOptionModel(options, env.simulate)
     raise NotImplementedError(f"Unknown option model: {name}")
 
 
@@ -55,11 +58,11 @@ class _OracleOptionModel(_OptionModelBase):
     Runs options through this simulator to figure out the next state.
     """
 
-    def __init__(self, env: BaseEnv) -> None:
+    def __init__(self, options: Set[ParameterizedOption],
+                 simulator: Callable[[State, Action], State]) -> None:
         super().__init__()
-        gt_options = get_gt_options(env.get_name())
-        self._name_to_parameterized_option = {o.name: o for o in gt_options}
-        self._simulator = env.simulate
+        self._name_to_parameterized_option = {o.name: o for o in options}
+        self._simulator = simulator
 
     def get_next_state_and_num_actions(self, state: State,
                                        option: _Option) -> Tuple[State, int]:

tests/approaches/test_oracle_approach.py

@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+from gym.spaces import Box
 
 import predicators.envs.pddl_env
 from predicators import utils
@@ -718,3 +719,91 @@ def test_playroom_get_gt_nsrts():
         state, train_task.goal, rng)
     movedoortodoor_action = movedoortodoor_option.policy(state)
     assert env.action_space.contains(movedoortodoor_action.arr)
+
+
+def test_external_oracle_approach():
+    """Test that it's possible for an external user of predicators to define
+    their own environment and NSRTs and use the oracle approach."""
+
+    utils.reset_config({"num_train_tasks": 2, "num_test_tasks": 2})
+
+    class _ExternalBlocksEnv(BlocksEnv):
+        """To make sure that the test doesn't pass without using the new NSRTs,
+        reverse the action space."""
+
+        @classmethod
+        def get_name(cls) -> str:
+            return "external_blocks"
+
+        @property
+        def action_space(self) -> Box:
+            original_space = super().action_space
+            return Box(original_space.low[::-1],
+                       original_space.high[::-1],
+                       dtype=np.float32)
+
+        def simulate(self, state, action):
+            # Need to rewrite these lines here to avoid assertion in simulate
+            # that uses action_space.
+            x, y, z, fingers = action.arr[::-1]
+            # Infer which transition function to follow
+            if fingers < 0.5:
+                return self._transition_pick(state, x, y, z)
+            if z < self.table_height + self._block_size:
+                return self._transition_putontable(state, x, y, z)
+            return self._transition_stack(state, x, y, z)
+
+    env = _ExternalBlocksEnv()
+
+    # Create external options by modifying blocks options.
+    options = set()
+    old_option_to_new_option = {}
+
+    def _reverse_policy(original_policy):
+
+        def new_policy(state, memory, objects, params):
+            action = original_policy(state, memory, objects, params)
+            return Action(action.arr[::-1])
+
+        return new_policy
+
+    original_options = get_gt_options("blocks")
+    for option in original_options:
+        new_policy = _reverse_policy(option.policy)
+        new_option = ParameterizedOption(f"external_{option.name}",
+                                         option.types, option.params_space,
+                                         new_policy, option.initiable,
+                                         option.terminal)
+        options.add(new_option)
+        old_option_to_new_option[option] = new_option
+
+    # Create the option model.
+    option_model = _OracleOptionModel(options, env.simulate)
+
+    # Create external NSRTs by just modifying blocks NSRTs.
+    nsrts = set()
+    for nsrt in get_gt_nsrts("blocks", env.predicates, original_options):
+        nsrt_option = old_option_to_new_option[nsrt.option]
+        sampler = nsrt._sampler  # pylint: disable=protected-access
+        new_nsrt = NSRT(f"external_{nsrt.name}", nsrt.parameters,
+                        nsrt.preconditions, nsrt.add_effects,
+                        nsrt.delete_effects, nsrt.ignore_effects, nsrt_option,
+                        nsrt.option_vars, sampler)
+        nsrts.add(new_nsrt)
+
+    # Create oracle approach.
+    train_tasks = [t.task for t in env.get_train_tasks()]
+    approach = OracleApproach(env.predicates,
+                              options,
+                              env.types,
+                              env.action_space,
+                              train_tasks,
+                              nsrts=nsrts,
+                              option_model=option_model)
+
+    # Get a policy for the first task.
+    task = train_tasks[0]
+    policy = approach.solve(task, timeout=500)
+
+    # Verify the policy.
+    assert _policy_solves_task(policy, task, env.simulate)

tests/explorers/test_active_sampler_explorer.py

@@ -25,9 +25,9 @@ def test_active_sampler_explorer():
         "sampler_learner": "oracle",
     })
     env = RegionalBumpyCoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     ground_op_hist = {}
     competence_models = {}
@@ -168,9 +168,9 @@ def test_active_sampler_explorer():
         "active_sampler_explore_task_strategy": "success_rate",
     })
     env = RegionalBumpyCoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     ground_op_hist = {}
     competence_models = {}

tests/explorers/test_base_explorer.py

@@ -12,9 +12,9 @@ def test_create_explorer():
     """Tests for create_explorer."""
     utils.reset_config({"env": "cover"})
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     # Greedy lookahead explorer.
     state_score_fn = lambda _1, _2: 0.0

tests/explorers/test_exploit_bilevel_planning_explorer.py

@@ -15,9 +15,9 @@ def test_exploit_bilevel_planning_explorer():
         "explorer": "exploit_planning",
     })
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     explorer = create_explorer("exploit_planning", env.predicates,
                                get_gt_options(env.get_name()), env.types,

tests/explorers/test_glib_explorer.py

@@ -17,9 +17,9 @@ def test_glib_explorer(target_predicate):
         "cover_initial_holding_prob": 0.0,
     })
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     # For testing purposes, score everything except target predicate low.
     score_fn = lambda atoms: target_predicate in str(atoms)
@@ -85,9 +85,9 @@ def test_glib_explorer_failure_cases():
         "explorer": "glib",
     })
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     score_fn = lambda _: 0.0
     task_idx = 0

tests/explorers/test_greedy_lookahead_explorer.py

@@ -18,9 +18,9 @@ def test_greedy_lookahead_explorer(target_predicate):
         "cover_initial_holding_prob": 0.0,
     })
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     # For testing purposes, score everything except target predicate low.
     score_fn = lambda atoms, _: target_predicate in str(atoms)
@@ -63,9 +63,9 @@ def test_greedy_lookahead_explorer_failure_cases():
         "explorer": "greedy_lookahead",
     })
     env = CoverEnv()
-    nsrts = get_gt_nsrts(env.get_name(), env.predicates,
-                         get_gt_options(env.get_name()))
-    option_model = _OracleOptionModel(env)
+    options = get_gt_options(env.get_name())
+    nsrts = get_gt_nsrts(env.get_name(), env.predicates, options)
+    option_model = _OracleOptionModel(options, env.simulate)
     train_tasks = [t.task for t in env.get_train_tasks()]
     state_score_fn = lambda _1, _2: 0.0
     task_idx = 0

tests/test_option_model.py

@@ -53,9 +53,9 @@ def simulate(state, action):
 
     class _MockOracleOptionModel(_OracleOptionModel):
 
-        def __init__(self, env) -> None:  # pylint: disable=super-init-not-called
+        def __init__(self, simulator) -> None:  # pylint: disable=super-init-not-called
             self._name_to_parameterized_option = {"Pick": parameterized_option}
-            self._simulator = env.simulate
+            self._simulator = simulator
 
     # Mock option.
     parameterized_option = ParameterizedOption("Pick", [], params_space,
@@ -76,7 +76,7 @@ def __init__(self, env) -> None:  # pylint: disable=super-init-not-called
         obj4: [8, 9, 10],
         obj9: [11, 12, 13]
     })
-    model = _MockOracleOptionModel(env)
+    model = _MockOracleOptionModel(env.simulate)
     next_state, num_act = model.get_next_state_and_num_actions(state, option1)
     assert num_act == 5
     # Test that the option's memory has not been updated.
@@ -143,15 +143,15 @@ def simulate(state, action):
 
     class _MockOracleOptionModel(_OracleOptionModel):
 
-        def __init__(self, env) -> None:  # pylint: disable=super-init-not-called
+        def __init__(self, simulator) -> None:  # pylint: disable=super-init-not-called
             self._name_to_parameterized_option = {
                 "InfiniteLearnedOption": infinite_param_opt
             }
-            self._simulator = env.simulate
+            self._simulator = simulator
 
     infinite_option = infinite_param_opt.ground([], params1)
     env = _NoopMockEnv()
-    model = _MockOracleOptionModel(env)
+    model = _MockOracleOptionModel(env.simulate)
     next_state, num_act = model.get_next_state_and_num_actions(
         state, infinite_option)
     assert next_state.allclose(state)
@@ -163,7 +163,7 @@ def __init__(self, env) -> None:  # pylint: disable=super-init-not-called
         "max_num_steps_option_rollout": 5,
     })
 
-    model = _MockOracleOptionModel(env)
+    model = _MockOracleOptionModel(env.simulate)
     _, num_act = model.get_next_state_and_num_actions(state, infinite_option)
     assert num_act == 5