aicrowd.json

@@ -2,7 +2,7 @@
   "challenge_id": "aicrowd-neurips-2020-minerl-challenge",
   "grader_id": "aicrowd-neurips-2020-minerl-challenge",
   "authors": ["minerl_sqil_baseline"],
-  "tags": "IL",
+  "tags": "RL",
   "description": "Test Model for MineRL Challenge",
-  "gpu": false
+  "gpu": true
 }

environment.yml

+name: minerl
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - python=3.6
+  - pip
+  - pytorch
+  - py-opencv
+  - pip:
+    - crowdai_api
+    - minerl
+    - coloredlogs
+    - matplotlib
+    - pyro4
+    - pfrl
+    - scikit-learn

mod/agents/sqil.py

@@ -106,6 +106,8 @@ def _batch_reset_recurrent_states_when_episodes_end(
 
 def load_experiences_from_demonstrations(
         expert_dataset, batch_size, reward=1):
+    if expert_dataset is None:
+        raise ValueError("Expert dataset must be provided.")
     ret = []
     for _ in range(batch_size):
         ob, act, _, next_ob, done = expert_dataset.sample()
@@ -162,7 +164,6 @@ class RewardBasedSampler:
         n_samples = [0 for _ in range(len(self.reward_boundaries) + 1)]
         for frame in experiences:
             n_samples[self._policy_index(frame[0]['state'])] += 1
-        print(n_samples)
         ret = []
         for rbuf, n_sample in zip(self.replay_buffers, n_samples):
             samples = rbuf.sample(n_sample)
@@ -235,8 +236,6 @@ class SQIL(agent.AttributeSavingMixin, agent.BatchAgent):
         recurrent=False,
         reward_boundaries=None,  # specific to options
     ):
-        if expert_dataset is None:
-            raise ValueError("Expert dataset must be provided.")
         self.expert_dataset = expert_dataset
 
         self.model = q_function
@@ -290,7 +289,7 @@ class SQIL(agent.AttributeSavingMixin, agent.BatchAgent):
         self.reward_scale = reward_scale
         self.experience_lambda = experience_lambda
 
-        if reward_boundaries is not None:
+        if reward_boundaries is not None and self.expert_dataset is not None:
             self.reward_based_sampler = RewardBasedSampler(
                 self.expert_dataset,
                 reward_boundaries,
@@ -327,21 +326,6 @@ class SQIL(agent.AttributeSavingMixin, agent.BatchAgent):
         # cumulative_steps counts the overall steps during the training.
         return self._cumulative_steps
 
-    def _setup_actor_learner_training(self, n_actors, actor_update_interval):
-        assert actor_update_interval > 0
-
-        self.actor_update_interval = actor_update_interval
-        self.update_counter = 0
-
-        # Make a copy on shared memory and share among actors and the poller
-        shared_model = copy.deepcopy(self.model).cpu()
-        shared_model.share_memory()
-
-        # Pipes are used for infrequent communication
-        learner_pipes, actor_pipes = list(zip(*[mp.Pipe() for _ in range(n_actors)]))
-
-        return (shared_model, learner_pipes, actor_pipes)
-
     def sync_target_network(self):
         """Synchronize target network with current network."""
         if self.target_model is None:
@@ -663,158 +647,6 @@ class SQIL(agent.AttributeSavingMixin, agent.BatchAgent):
             return False
         return True
 
-    def _poll_pipe(self, actor_idx, pipe, replay_buffer_lock, exception_event):
-        if pipe.closed:
-            return
-        try:
-            while pipe.poll() and not exception_event.is_set():
-                cmd, data = pipe.recv()
-                if cmd == "get_statistics":
-                    assert data is None
-                    with replay_buffer_lock:
-                        stats = self.get_statistics()
-                    pipe.send(stats)
-                elif cmd == "load":
-                    self.load(data)
-                    pipe.send(None)
-                elif cmd == "save":
-                    self.save(data)
-                    pipe.send(None)
-                elif cmd == "transition":
-                    with replay_buffer_lock:
-                        if "env_id" not in data:
-                            data["env_id"] = actor_idx
-                        self.replay_buffer.append(**data)
-                        self._cumulative_steps += 1
-                elif cmd == "stop_episode":
-                    idx = actor_idx if data is None else data
-                    with replay_buffer_lock:
-                        self.replay_buffer.stop_current_episode(env_id=idx)
-                        stats = self.get_statistics()
-                    pipe.send(stats)
-
-                else:
-                    raise RuntimeError("Unknown command from actor: {}".format(cmd))
-        except EOFError:
-            pipe.close()
-        except Exception:
-            self.logger.exception("Poller loop failed. Exiting")
-            exception_event.set()
-
-    def _learner_loop(
-        self,
-        shared_model,
-        pipes,
-        replay_buffer_lock,
-        stop_event,
-        exception_event,
-        n_updates=None,
-    ):
-        try:
-            update_counter = 0
-            # To stop this loop, call stop_event.set()
-            while not stop_event.is_set():
-                # Update model if possible
-                if not self._can_start_replay():
-                    continue
-                if n_updates is not None:
-                    assert self.optim_t <= n_updates
-                    if self.optim_t == n_updates:
-                        stop_event.set()
-                        break
-
-                if self.recurrent:
-                    with replay_buffer_lock:
-                        episodes = self.replay_buffer.sample_episodes(
-                            self.minibatch_size, self.episodic_update_len
-                        )
-                    self.update_from_episodes(episodes)
-                else:
-                    with replay_buffer_lock:
-                        transitions = self.replay_buffer.sample(self.minibatch_size)
-                    self.update(transitions)
-
-                # Update the shared model. This can be expensive if GPU is used
-                # since this is a DtoH copy, so it is updated only at regular
-                # intervals.
-                update_counter += 1
-                if update_counter % self.actor_update_interval == 0:
-                    self.update_counter += 1
-                    shared_model.load_state_dict(self.model.state_dict())
-
-                # To keep the ratio of target updates to model updates,
-                # here we calculate back the effective current timestep
-                # from update_interval and number of updates so far.
-                effective_timestep = self.optim_t * self.update_interval
-                # We can safely assign self.t since in the learner
-                # it isn't updated by any other method
-                self.t = effective_timestep
-                if effective_timestep % self.target_update_interval == 0:
-                    self.sync_target_network()
-        except Exception:
-            self.logger.exception("Learner loop failed. Exiting")
-            exception_event.set()
-
-    def _poller_loop(
-        self, shared_model, pipes, replay_buffer_lock, stop_event, exception_event
-    ):
-        # To stop this loop, call stop_event.set()
-        while not stop_event.is_set() and not exception_event.is_set():
-            time.sleep(1e-6)
-            # Poll actors for messages
-            for i, pipe in enumerate(pipes):
-                self._poll_pipe(i, pipe, replay_buffer_lock, exception_event)
-
-    def setup_actor_learner_training(
-        self, n_actors, n_updates=None, actor_update_interval=8
-    ):
-        (shared_model, learner_pipes, actor_pipes) = self._setup_actor_learner_training(
-            n_actors, actor_update_interval
-        )
-        exception_event = mp.Event()
-
-        def make_actor(i):
-            return pfrl.agents.StateQFunctionActor(
-                pipe=actor_pipes[i],
-                model=shared_model,
-                explorer=self.explorer,
-                phi=self.phi,
-                batch_states=self.batch_states,
-                logger=self.logger,
-                recurrent=self.recurrent,
-            )
-
-        replay_buffer_lock = mp.Lock()
-
-        poller_stop_event = mp.Event()
-        poller = pfrl.utils.StoppableThread(
-            target=self._poller_loop,
-            kwargs=dict(
-                shared_model=shared_model,
-                pipes=learner_pipes,
-                replay_buffer_lock=replay_buffer_lock,
-                stop_event=poller_stop_event,
-                exception_event=exception_event,
-            ),
-            stop_event=poller_stop_event,
-        )
-
-        learner_stop_event = mp.Event()
-        learner = pfrl.utils.StoppableThread(
-            target=self._learner_loop,
-            kwargs=dict(
-                shared_model=shared_model,
-                pipes=learner_pipes,
-                replay_buffer_lock=replay_buffer_lock,
-                stop_event=learner_stop_event,
-                n_updates=n_updates,
-                exception_event=exception_event,
-            ),
-            stop_event=learner_stop_event,
-        )
-
-        return make_actor, learner, poller, exception_event
-
     def stop_episode(self):
         if self.recurrent:
             self.test_recurrent_states = None

mod/cached_kmeans.py

@@ -14,47 +14,24 @@ class _KMeansCacheNotFound(FileNotFoundError):
     pass
 
 
-class BoundedLengthMemory:
-    def __init__(self, maxlen, random_state):
-        self.maxlen = maxlen
-        self.t = 0
-        self._rand = np.random.RandomState(random_state)
-        self.memory = []
-
-    def __call__(self):
-        return np.array(self.memory)
-
-    def append(self, action):
-        self.t += 1
-        if self.maxlen is None or len(self.memory) < self.maxlen:
-            self.memory.append(action)
-        else:
-            idx = self._rand.randint(self.t)
-            if idx < self.maxlen:
-                self.memory[idx] = action
-
-
 def cached_kmeans(cache_dir, env_id, n_clusters, random_state,
-                  subtask_reward_max=None, maxlen_each=None,
-                  only_vector_converter=False):
+                  sample_by_trajectory=False, only_vector_converter=False):
+    if only_vector_converter and not sample_by_trajectory:
+        raise ValueError("The vector converter option must be selected with the ascending order option.")
     if cache_dir is None:  # ignore cache
         logger.info('Load dataset & do kmeans')
         kmeans = _do_kmeans(env_id=env_id, n_clusters=n_clusters,
                             random_state=random_state,
-                            subtask_reward_max=subtask_reward_max,
+                            sample_by_trajectory=sample_by_trajectory,
                             only_vector_converter=only_vector_converter)
     else:
-        if subtask_reward_max is None:
-            name_subtask_reward_max = ''
-        else:
-            name_subtask_reward_max = '_{}'.format(subtask_reward_max)
         if only_vector_converter:
-            filename = 'kmeans_vector_converter{}.joblib'.format(name_subtask_reward_max)  # noqa
-        elif maxlen_each is not None:
-            filename = 'kmeans_balanced_{}{}.joblib'.format(
-                           maxlen_each, name_subtask_reward_max)
+            filename = 'kmeans_vector_converter.joblib'
         else:
-            filename = 'kmeans{}.joblib'.format(name_subtask_reward_max)
+            if sample_by_trajectory:
+                filename = 'kmeans_normal.joblib'
+            else:
+                filename = 'kmeans.joblib'
         filepath = os.path.join(cache_dir, env_id, f'n_clusters_{n_clusters}', f'random_state_{random_state}', filename)
         try:
             kmeans = _load_kmeans_result_cache(filepath)
@@ -63,18 +40,17 @@ def cached_kmeans(cache_dir, env_id, n_clusters, random_state,
             logger.info('kmeans cache not found. Load dataset & do kmeans & save result as cache')
             kmeans = _do_kmeans(env_id=env_id, n_clusters=n_clusters,
                                 random_state=random_state,
-                                subtask_reward_max=subtask_reward_max,
-                                maxlen_each=maxlen_each,
+                                sample_by_trajectory=sample_by_trajectory,
                                 only_vector_converter=only_vector_converter)
             _save_kmeans_result_cache(kmeans, filepath)
     return kmeans
 
 
-def _do_kmeans(env_id, n_clusters, random_state, subtask_reward_max,
-               maxlen_each, only_vector_converter):
+def _do_kmeans(env_id, n_clusters, random_state, sample_by_trajectory,
+               only_vector_converter):
     logger.debug(f'loading data...')
     dat = minerl.data.make(env_id)
-    if subtask_reward_max is None and maxlen_each is None:
+    if not sample_by_trajectory:
         act_vectors = []
         for ob, act, _, next_ob, _ in tqdm.tqdm(dat.batch_iter(batch_size=16, seq_len=32, num_epochs=1, preload_buffer_size=32, seed=random_state)):
             if only_vector_converter:
@@ -85,8 +61,8 @@ def _do_kmeans(env_id, n_clusters, random_state, subtask_reward_max,
         acts = np.concatenate(act_vectors).reshape(-1, 64)
     else:
         episode_names = dat.get_trajectory_names()
-        mem_normal = BoundedLengthMemory(maxlen=maxlen_each, random_state=random_state)
-        mem_vc = BoundedLengthMemory(maxlen=maxlen_each, random_state=random_state)
+        mem_normal = []
+        mem_vc = []
         for episode_name in episode_names:
             traj = dat.load_data(episode_name)
             dn = False
@@ -94,17 +70,15 @@ def _do_kmeans(env_id, n_clusters, random_state, subtask_reward_max,
             while not dn:
                 ob, act, rw, next_ob, dn = next(traj)
                 current_reward_sum += rw
-                if subtask_reward_max is not None and current_reward_sum >= subtask_reward_max:
-                    dn = True
                 if np.allclose(ob['vector'], next_ob['vector']):
                     # Ignore the case when the action does not change observation$vector.
                     mem_normal.append(act['vector'])
                 else:
                     mem_vc.append(act['vector'])
         if only_vector_converter:
-            acts = mem_vc().reshape(-1, 64)
+            acts = np.array(mem_vc).reshape(-1, 64)
         else:
-            acts = np.concatenate((mem_normal(), mem_vc()), axis=0).reshape(-1, 64)
+            acts = np.concatenate((np.array(mem_normal), np.array(mem_vc)), axis=0).reshape(-1, 64)
     logger.debug(f'loading data... done.')
     logger.debug(f'executing keamns...')
     kmeans = KMeans(n_clusters=n_clusters, random_state=random_state).fit(acts)

mod/data/action_converter.py

@@ -59,12 +59,9 @@ class KMeansActionConverter:
     def __init__(self, kmeans):
         self.kmeans = kmeans
         keys = ['craft', 'nearbyCraft', 'nearbySmelt', 'equip', 'place', 'attack', 'forward', 'camera']
-        print(keys)
         for center in kmeans.cluster_centers_:
             action = {'vector': center}
             dict = minerl.herobraine.envs.MINERL_OBTAIN_DIAMOND_OBF_V0.unwrap_action(action)
-            # print(dict)
-            print([dict[k] for k in keys])
 
     def __call__(self, action):
         index = self.kmeans.predict(action['vector'])

mod/data/observation_converter.py

@@ -45,7 +45,7 @@ class PoVOnlyConverter:
     def convert_space(self, space):
         pov = space['pov']
         return gym.spaces.Box(
-            low=-255, high=255,
+            low=0, high=255,
             shape=pov.shape,
             dtype=np.float32)
 
@@ -57,14 +57,14 @@ class VectorCombineConverter:
         scale = 1 / 255
         pov, vector = observation['pov'], observation['vector']
         num_elem = pov.shape[-3] * pov.shape[-2]
-        vector_channel = np.tile(vector, num_elem // vector.shape[-1]).reshape(*pov.shape[:-1], -1)  # noqa
+        vector_channel = np.tile((vector + 1) / 2, num_elem // vector.shape[-1]).reshape(*pov.shape[:-1], -1)  # noqa
         return np.concatenate([pov, vector_channel / scale], axis=-1)
 
     def convert_space(self, space):
         pov, vector = space['pov'], space['vector']
         num_new_channel = 1
         return gym.spaces.Box(
-            low=-255, high=255,
+            low=0, high=255,
             shape=(*pov.shape[:-1], pov.shape[-1] + num_new_channel),
             dtype=np.float32)
 
@@ -95,25 +95,3 @@ class ScaledFloatConverter:
             low=(low * self.scale).astype(np.float32),
             high=(high * self.scale).astype(np.float32),
             shape=low.shape, dtype=np.float32)
-
-
-
-if __name__ == '__main__':
-    """Testing"""
-    input = {
-        'pov': np.arange(18 * 64 * 64).reshape(2, 3, 64, 64, 3).astype(np.uint8),
-        'vector': np.arange(6 * 64).reshape(2, 3, 64) / 64,
-    }
-    print(input['pov'])
-    gray_scaled = GrayScaleConverter()(input)
-    for i in range(2):
-        for j in range(3):
-            assert np.allclose(
-                gray_scaled['pov'][i, j],
-                np.expand_dims(
-                    cv2.cvtColor(input['pov'][i, j], cv2.COLOR_RGB2GRAY),
-                    -1))
-    combined = VectorCombineConverter()(gray_scaled)
-    axis_moved = MoveAxisConverter()(combined)
-    print(axis_moved.shape)
-    assert axis_moved.shape == (2, 3, 2, 64, 64)

mod/data/pipeline_wrapper.py

-from __future__ import division
-from __future__ import print_function
-from __future__ import unicode_literals
-from __future__ import absolute_import
-from builtins import *  # NOQA
-from future import standard_library
-standard_library.install_aliases()  # NOQA
-
 from logging import getLogger
 import numpy as np
 from collections import deque, OrderedDict
@@ -58,14 +50,12 @@ def _get_aggregated_action(obs, actions, next_obs, frameskip):
 class DataPipelineWrapper:
     def __init__(self, dataset, observation_converters,
                  action_converters, frameskip=1, framestack=1,
-                 subtask_reward_max=None, append_reward_channel=False,
-                 reward_scale=1./1024):
+                 append_reward_channel=False, reward_scale=1./1024):
         self.dataset = dataset
         self.observation_converters = observation_converters
         self.action_converters = action_converters
         self.frameskip = frameskip
         self.framestack = framestack
-        self.subtask_reward_max = subtask_reward_max
         self.append_reward_channel = append_reward_channel
         self.episode_names = self.dataset.get_trajectory_names()
         self.current_episode_name = np.random.choice(self.episode_names)
@@ -81,8 +71,6 @@ class DataPipelineWrapper:
         while len(rewards[0]) < self.frameskip * self.framestack:
             ob, ac, rw, nob, dn = next(self.batch_loader)
             self.current_reward_sum += rw
-            if self.subtask_reward_max is not None and self.current_reward_sum >= self.subtask_reward_max:
-                dn = True
 
             obs_pov[0].append(ob['pov'])
             obs_vector[0].append(ob['vector'])

mod/env_wrappers.py

@@ -23,13 +23,14 @@ def wrap_env(
         randomize_action, eval_epsilon,
         action_choices,
         action_choices_vector_converter=None,
-        append_reward_channel=True):
+        append_reward_channel=False):
     # wrap env: time limit...
-    if isinstance(env, gym.wrappers.TimeLimit):
-        logger.info('Detected `gym.wrappers.TimeLimit`! Unwrap it and re-wrap our own time limit.')
-        env = env.env
-        max_episode_steps = env.spec.max_episode_steps
-        env = ContinuingTimeLimit(env, max_episode_steps=max_episode_steps)
+    if not test:
+        if isinstance(env, gym.wrappers.TimeLimit):
+            logger.info('Detected `gym.wrappers.TimeLimit`! Unwrap it and re-wrap our own time limit.')
+            env = env.env
+            max_episode_steps = env.spec.max_episode_steps
+            env = ContinuingTimeLimit(env, max_episode_steps=max_episode_steps)
 
     # wrap env: observation...
     # NOTE: wrapping order matters!
@@ -228,7 +229,7 @@ class PoVWithVectorWrapper(gym.ObservationWrapper):
 
     def observation(self, observation):
         pov = observation['pov']
-        vector_scaled = observation['vector'] / self._vector_scale
+        vector_scaled = (observation['vector'] + 1) / 2 / self._vector_scale  # to (0, 255)
         num_elem = pov.shape[-3] * pov.shape[-2]
         vector_channel = np.tile(vector_scaled, num_elem // vector_scaled.shape[-1]).reshape(*pov.shape[:-1], -1)  # noqa
         return np.concatenate([pov, vector_channel], axis=-1)

mod/mock_env.py

-import minerl
-import copy
-from logging import getLogger
-from collections import OrderedDict, deque
-import os
-
-import gym
-import numpy as np
-import cv2
-
-from pfrl.wrappers import ContinuingTimeLimit, RandomizeAction, Monitor
-from pfrl.wrappers.atari_wrappers import ScaledFloatFrame, LazyFrames
-
-cv2.ocl.setUseOpenCL(False)
-logger = getLogger(__name__)
-
-datasetA = minerl.data.make("MineRLObtainDiamondVectorObf-v0", num_workers=1)
-datasetB = minerl.data.make("MineRLObtainDiamond-v0", num_workers=1)
-traj_name="v3_villainous_black_eyed_peas_loch_ness_monster-2_3997-48203"
-itrA = datasetA.load_data(traj_name)
-itrB = datasetB.load_data(traj_name)
-# itr = dataset.batch_iter(1, 1, -1)
-
-class MockEnv(gym.Env):
-    def __init__(self, kmeans=None):
-        self.mock_obf = OrderedDict([
-            ('pov', np.random.randint(256, size=(64, 64, 3), dtype=np.uint8)),
-            ('vector', np.random.rand(64).astype(np.float32)),
-        ])
-        self.observation_space = gym.spaces.Dict(
-            OrderedDict([
-                ('pov', gym.spaces.Box(low=0, high=255, shape=(64, 64, 3), dtype=np.uint8)),
-                ('vector', gym.spaces.Box(low=-1, high=1, shape=(64,), dtype=np.float32)),
-            ])
-        )
-        self.action_space = gym.spaces.Dict(
-            OrderedDict([
-                ('vector', gym.spaces.Box(low=-1, high=1, shape=(64,), dtype=np.float32)),
-            ])
-        )
-        self.count = 0
-        self.kmeans = kmeans
-
-    def reset(self):
-        self.count = 0
-        return self.mock_obf
-
-    def step(self, action):
-        """
-        action = {'vector': np.random.rand(64) * 2 - 1}
-        for i in range(20000):
-            action = next(itrA)[1]
-            print("MineRLObtainDiamondVectorObf")
-            print(minerl.herobraine.envs.MINERL_OBTAIN_DIAMOND_OBF_V0.unwrap_action(action))
-            if self.kmeans is not None:
-                nearest_idx = self.kmeans.predict([action['vector']])[0]
-                recov = self.kmeans.cluster_centers_[nearest_idx]
-                print("MineRLObtainDiamondVectorObf (nearest k-means)")
-                print(minerl.herobraine.envs.MINERL_OBTAIN_DIAMOND_OBF_V0.unwrap_action({'vector': recov}))
-            action_b = next(itrB)[1]
-            print("MineRLObtainDiamond")
-            print(action_b)
-            while not (action_b['craft'] == 'none'):
-                pass
-            print("_____")
-        assert False
-        """
-        self.count += 1
-        return self.mock_obf, np.random.rand(), self.count > 8000, {}

mod/reward_boundary_calculator.py

@@ -13,19 +13,13 @@ class _CacheNotFound(FileNotFoundError):
     pass
 
 
-def cached_reward_boundary(cache_dir, env_id, n_groups, random_state,
-                           subtask_reward_max=None):
+def cached_reward_boundary(cache_dir, env_id, n_groups, random_state):
     if cache_dir is None:  # ignore cache
         logger.info('Load dataset & calculate boundaries')
         boundaries = _calc_boundaries(env_id=env_id, n_groups=n_groups,
-                                      random_state=random_state,
-                                      subtask_reward_max=subtask_reward_max)
+                                      random_state=random_state)
     else:
-        if subtask_reward_max is None:
-            name_subtask_reward_max = ''
-        else:
-            name_subtask_reward_max = '_{}'.format(subtask_reward_max)
-        filename = 'reward_boundaries{}.joblib'.format(name_subtask_reward_max)
+        filename = 'reward_boundaries.joblib'
         filepath = os.path.join(cache_dir, env_id, f'n_groups_{n_groups}', f'random_state_{random_state}', filename)
         try:
             boundaries = _load_result_cache(filepath)
@@ -33,15 +27,13 @@ def cached_reward_boundary(cache_dir, env_id, n_groups, random_state,
         except _CacheNotFound:
             logger.info('boundary cache not found. Load dataset & calculate boundaries & save result as cache')
             boundaries = _calc_boundaries(env_id=env_id, n_groups=n_groups,
-                                          random_state=random_state,
-                                          subtask_reward_max=subtask_reward_max)
+                                          random_state=random_state)
             _save_result_cache(boundaries, filepath)
-    print(boundaries)
     logger.debug(boundaries)
     return boundaries
 
 
-def _calc_boundaries(env_id, n_groups, random_state, subtask_reward_max):
+def _calc_boundaries(env_id, n_groups, random_state):
     logger.debug(f'loading data...')
     dat = minerl.data.make(env_id)
     episode_names = dat.get_trajectory_names()
@@ -65,7 +57,6 @@ def _calc_boundaries(env_id, n_groups, random_state, subtask_reward_max):
         cnt += 1
     distrib.append((prev, cnt))
 
-    print(distrib)
     # Find boundaries
     def separate(distrib, max_region_size):
         region_size = None
@@ -86,7 +77,6 @@ def _calc_boundaries(env_id, n_groups, random_state, subtask_reward_max):
             cand_min = half + 1
         else:
             cand_max = half
-    print(cand_min)
 
     return separate(distrib, cand_min)[1:]
 

mod/sqil.py

@@ -114,8 +114,6 @@ def main(argv=None):
     parser.add_argument('--dual-kmeans', action='store_true', default=False,
                         help='Use dual kmeans of different clustering criteria.')
     parser.add_argument('--kmeans-n-clusters-vc', type=int, default=30, help='#clusters for Dual K-means of vector converters')
-    parser.add_argument('--balanced-maxlen', type=int, default=None,
-                        help='Number of observations for each of normal and vector converters')
 
     # SQIL specific settings
     parser.add_argument('--max-episode-len', type=int, default=None, help='Manual maximum episode length.')
@@ -123,10 +121,6 @@ def main(argv=None):
     parser.add_argument('--experience-lambda', type=float, default=1, help='Weight coefficient of batches from experiences.')
     parser.add_argument('--option-n-groups', type=int, default=1, help='Number of options to switch polices.')
 
-    # misc
-    parser.add_argument('--subtask-reward-max', type=int, default=None,
-                        help='Maximum reward of each episode. MAY NOT BE OFFICIALLY ALLOWED TO USE.')
-
     args = parser.parse_args(args=argv)
 
     if args.remove_timestamp:
@@ -163,28 +157,25 @@ def _main(args):
 
     # K-Means
     if args.dual_kmeans:
-        if args.balanced_maxlen is not None:
-            raise ValueError('balanced_maxlen is unavailable in DualKmeans.')
         kmeans_normal = cached_kmeans(
             cache_dir=os.environ.get('KMEANS_CACHE'),
             env_id=args.env,
             n_clusters=args.kmeans_n_clusters,
             random_state=args.seed,
-            subtask_reward_max=args.subtask_reward_max,
+            sample_by_trajectory=True,
             only_vector_converter=False)
         kmeans_vector_converter = cached_kmeans(
             cache_dir=os.environ.get('KMEANS_CACHE'),
             env_id=args.env,
             n_clusters=args.kmeans_n_clusters_vc,
             random_state=args.seed,
-            subtask_reward_max=args.subtask_reward_max,
+            sample_by_trajectory=True,
             only_vector_converter=True)
     else:
         kmeans = cached_kmeans(
             cache_dir=os.environ.get('KMEANS_CACHE'),
             env_id=args.env,
             n_clusters=args.kmeans_n_clusters,
-            maxlen_each=args.balanced_maxlen,
             random_state=args.seed)
     if args.option_n_groups > 1:
         boundaries = cached_reward_boundary(
@@ -223,7 +214,6 @@ def _main(args):
             orig_data,
             observation_converters=observation_converters,
             action_converters=action_converters,
-            subtask_reward_max=args.subtask_reward_max,
             frameskip=args.frame_skip, framestack=args.frame_stack,
             append_reward_channel=(args.option_n_groups > 1),
             reward_scale=reward_channel_scale)

requirements.txt

-crowdai_api
-minerl
-coloredlogs
-matplotlib
-opencv-python==4.2.0.34
-pyro4
-scikit-learn
-torch
-pfrl

test.py

@@ -23,13 +23,12 @@ import coloredlogs
 coloredlogs.install(logging.DEBUG)
 
 # Agent settings
-GPU = -1
+GPU = 0
 
 ARCH = 'dueling'
 KMEANS_N_CLUSTERS = 30
 KMEANS_N_CLUSTERS_VC = 60
 KMEANS_SEED = 0
-SUBTASK_REWARD_MAX = 9999
 OPTION_N_GROUPS = 10
 FINAL_EPSILON = 0.01
 FINAL_EXPLORATION_FRAMES = 10 ** 6
@@ -188,7 +187,7 @@ class MineRLSQILBaselineAgent(MineRLAgentBase):
 
     def load_agent(self):
         boundaries = cached_reward_boundary(
-            cache_dir=os.environ.get('BOUNDARY_CACHE'),
+            cache_dir='./train/boundary_cache/',
             env_id=MINERL_GYM_ENV,
             n_groups=OPTION_N_GROUPS,
             random_state=KMEANS_SEED)
@@ -236,14 +235,14 @@ def main():
         env_id=MINERL_GYM_ENV,
         n_clusters=KMEANS_N_CLUSTERS,
         random_state=KMEANS_SEED,
-        subtask_reward_max=SUBTASK_REWARD_MAX,
+        sample_by_trajectory=True,
         only_vector_converter=False)
     kmeans_vector_converter = cached_kmeans(
         cache_dir='./train/kmeans_cache/',
         env_id=MINERL_GYM_ENV,
         n_clusters=KMEANS_N_CLUSTERS_VC,
         random_state=KMEANS_SEED,
-        subtask_reward_max=SUBTASK_REWARD_MAX,
+        sample_by_trajectory=True,
         only_vector_converter=True)
 
     def wrapper(env):

train.py

@@ -60,7 +60,7 @@ def main():
         '--eval-interval', '2500',
         '--eval-n-runs', '20',
         '--remove-timestamp',  # save to outdir/latest
-        '--dual-kmeans', '--subtask-reward-max', '9999',  # currently, it is not used for bounding observation but for avoiding conflict of kmeans_cache implementation of other baselines.
+        '--dual-kmeans',
         '--kmeans-n-clusters-vc', '60',
         '--option-n-groups', '10'])