copy minerl2020-playground/src/mod

mod/cached_kmeans.py

+from logging import getLogger
+import os
+
+import tqdm
+import numpy as np
+from sklearn.cluster import KMeans
+import joblib
+import minerl
+
+logger = getLogger(__name__)
+
+
+class _KMeansCacheNotFound(FileNotFoundError):
+    pass
+
+
+def cached_kmeans(cache_dir, env_id, n_clusters, random_state):
+    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)
+    else:
+        filepath = os.path.join(cache_dir, env_id, f'n_clusters_{n_clusters}', f'random_state_{random_state}', 'kmeans.joblib')
+        try:
+            kmeans = _load_kmeans_result_cache(filepath)
+            logger.info('found kmeans cache')
+        except _KMeansCacheNotFound:
+            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)
+            _save_kmeans_result_cache(kmeans, filepath)
+    return kmeans
+
+
+def _do_kmeans(env_id, n_clusters, random_state):
+    logger.debug(f'loading data...')
+    dat = minerl.data.make(env_id)
+    act_vectors = []
+    for _, act, _, _, _ in tqdm.tqdm(dat.batch_iter(batch_size=16, seq_len=32, num_epochs=1, preload_buffer_size=32, seed=random_state)):
+        act_vectors.append(act['vector'])
+    acts = np.concatenate(act_vectors).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)
+    logger.debug(f'executing keamns... done.')
+    return kmeans
+
+
+# def _describe_kmeans_result(kmeans):
+#     result = [(obf_a, minerl.herobraine.envs.MINERL_TREECHOP_OBF_V0.unwrap_action({'vector': obf_a})) for obf_a in kmeans.cluster_centers_]
+#     logger.debug(result)
+#     return result
+
+
+def _save_kmeans_result_cache(kmeans, filepath):
+    os.makedirs(os.path.dirname(filepath), exist_ok=True)
+    joblib.dump(kmeans, filepath)
+    logger.info(f'saved kmeans {filepath}')
+
+
+def _load_kmeans_result_cache(filepath):
+    if not os.path.exists(filepath):
+        raise _KMeansCacheNotFound
+    logger.debug(f'loading kmeans {filepath}')
+    return joblib.load(filepath)

mod/dqn_family.py

+import os
+import logging
+import argparse
+
+import numpy as np
+import torch
+import minerl  # noqa: register MineRL envs as Gym envs.
+import gym
+
+import pfrl
+
+
+# local modules
+import sys
+sys.path.append(os.path.abspath(os.path.join(__file__, os.pardir)))
+import utils
+from env_wrappers import wrap_env
+from q_functions import parse_arch
+from cached_kmeans import cached_kmeans
+
+logger = logging.getLogger(__name__)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    env_choices = [
+        # basic envs
+        'MineRLTreechop-v0',
+        'MineRLNavigate-v0', 'MineRLNavigateDense-v0', 'MineRLNavigateExtreme-v0', 'MineRLNavigateExtremeDense-v0',
+        'MineRLObtainIronPickaxe-v0', 'MineRLObtainIronPickaxeDense-v0',
+        'MineRLObtainDiamond-v0', 'MineRLObtainDiamondDense-v0',
+        # obfuscated envs
+        'MineRLTreechopVectorObf-v0',
+        'MineRLNavigateVectorObf-v0', 'MineRLNavigateExtremeVectorObf-v0',
+        # MineRL data pipeline fails for these envs: https://github.com/minerllabs/minerl/issues/364
+        # 'MineRLNavigateDenseVectorObf-v0', 'MineRLNavigateExtremeDenseVectorObf-v0',
+        'MineRLObtainDiamondVectorObf-v0', 'MineRLObtainDiamondDenseVectorObf-v0',
+        'MineRLObtainIronPickaxeVectorObf-v0', 'MineRLObtainIronPickaxeDenseVectorObf-v0',
+        # for debugging
+        'MineRLNavigateDenseFixed-v0', 'MineRLObtainTest-v0',
+    ]
+    parser.add_argument('--env', type=str, choices=env_choices, required=True,
+                        help='MineRL environment identifier.')
+
+    # meta settings
+    parser.add_argument('--outdir', type=str, default='results',
+                        help='Directory path to save output files. If it does not exist, it will be created.')
+    parser.add_argument('--seed', type=int, default=0, help='Random seed [0, 2 ** 31)')
+    parser.add_argument('--gpu', type=int, default=0, help='GPU to use, set to -1 if no GPU.')
+    parser.add_argument('--demo', action='store_true', default=False)
+    parser.add_argument('--load', type=str, default=None)
+    parser.add_argument('--logging-level', type=int, default=20, help='Logging level. 10:DEBUG, 20:INFO etc.')
+    parser.add_argument('--eval-n-runs', type=int, default=3)
+    parser.add_argument('--monitor', action='store_true', default=False,
+                        help='Monitor env. Videos and additional information are saved as output files when evaluation.')
+
+    # training scheme (agent)
+    parser.add_argument('--agent', type=str, default='DQN', choices=['DQN', 'DoubleDQN', 'PAL', 'CategoricalDoubleDQN'])
+
+    # network architecture
+    parser.add_argument('--arch', type=str, default='dueling', choices=['dueling', 'distributed_dueling'],
+                        help='Network architecture to use.')
+
+    # update rule settings
+    parser.add_argument('--update-interval', type=int, default=4, help='Frequency (in timesteps) of network updates.')
+    parser.add_argument('--frame-skip', type=int, default=None, help='Number of frames skipped (None for disable).')
+    parser.add_argument('--gamma', type=float, default=0.99, help='Discount rate.')
+    parser.add_argument('--no-clip-delta', dest='clip_delta', action='store_false')
+    parser.set_defaults(clip_delta=True)
+    parser.add_argument('--num-step-return', type=int, default=1)
+    parser.add_argument('--lr', type=float, default=2.5e-4, help='Learning rate.')
+    parser.add_argument('--adam-eps', type=float, default=1e-8, help='Epsilon for Adam.')
+    parser.add_argument('--batch-accumulator', type=str, default='sum', choices=['sum', 'mean'], help='accumulator for batch loss.')
+
+    # observation conversion related settings
+    parser.add_argument('--gray-scale', action='store_true', default=False, help='Convert pov into gray scaled image.')
+    parser.add_argument('--frame-stack', type=int, default=None, help='Number of frames stacked (None for disable).')
+
+    # exploration related settings
+    parser.add_argument('--final-exploration-frames', type=int, default=10 ** 6,
+                        help='Timesteps after which we stop annealing exploration rate')
+    parser.add_argument('--final-epsilon', type=float, default=0.01, help='Final value of epsilon during training.')
+    parser.add_argument('--eval-epsilon', type=float, default=0.001, help='Exploration epsilon used during eval episodes.')
+    parser.add_argument('--noisy-net-sigma', type=float, default=None,
+                        help='NoisyNet explorer switch. This disables following options: '
+                        '--final-exploration-frames, --final-epsilon, --eval-epsilon')
+
+    # experience replay buffer related settings
+    parser.add_argument('--replay-capacity', type=int, default=10 ** 6, help='Maximum capacity for replay buffer.')
+    parser.add_argument('--replay-start-size', type=int, default=5 * 10 ** 4,
+                        help='Minimum replay buffer size before performing gradient updates.')
+    parser.add_argument('--prioritized', action='store_true', default=False, help='Use prioritized experience replay.')
+
+    # target network related settings
+    parser.add_argument('--target-update-interval', type=int, default=3 * 10 ** 4,
+                        help='Frequency (in timesteps) at which the target network is updated.')
+
+    # K-means related settings
+    parser.add_argument('--kmeans-n-clusters', type=int, default=30, help='#clusters for K-means')
+
+    args = parser.parse_args()
+
+    args.outdir = pfrl.experiments.prepare_output_dir(args, args.outdir)
+
+    log_format = '%(levelname)-8s - %(asctime)s - [%(name)s %(funcName)s %(lineno)d] %(message)s'
+    logging.basicConfig(filename=os.path.join(args.outdir, 'log.txt'), format=log_format, level=args.logging_level)
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(args.logging_level)
+    console_handler.setFormatter(logging.Formatter(log_format))
+    logging.getLogger('').addHandler(console_handler)  # add hander to the root logger
+
+    logger.info('Output files will be saved in {}'.format(args.outdir))
+
+    utils.log_versions()
+
+    try:
+        _main(args)
+    except:  # noqa
+        logger.exception('execution failed.')
+        raise
+
+
+def _main(args):
+    os.environ['MALMO_MINECRAFT_OUTPUT_LOGDIR'] = args.outdir
+
+    # Set a random seed used in ChainerRL.
+    pfrl.utils.set_random_seed(args.seed)
+
+    # Set different random seeds for train and test envs.
+    train_seed = args.seed  # noqa: never used in this script
+    test_seed = 2 ** 31 - 1 - args.seed
+
+    # K-Means
+    kmeans = cached_kmeans(
+        cache_dir=os.environ.get('KMEANS_CACHE'),
+        env_id=args.env,
+        n_clusters=args.kmeans_n_clusters,
+        random_state=args.seed)
+
+    # create & wrap env
+    def wrap_env_partial(env, test):
+        randomize_action = test and args.noisy_net_sigma is None
+        wrapped_env = wrap_env(
+            env=env, test=test,
+            env_id=args.env,
+            monitor=args.monitor, outdir=args.outdir,
+            frame_skip=args.frame_skip,
+            gray_scale=args.gray_scale, frame_stack=args.frame_stack,
+            randomize_action=randomize_action, eval_epsilon=args.eval_epsilon,
+            action_choices=kmeans.cluster_centers_)
+        return wrapped_env
+    logger.info('The first `gym.make(MineRL*)` may take several minutes. Be patient!')
+    core_env = gym.make(args.env)
+    # training env
+    env = wrap_env_partial(env=core_env, test=False)
+    # env.seed(int(train_seed))  # TODO: not supported yet
+    # evaluation env
+    eval_env = wrap_env_partial(env=core_env, test=True)
+    # env.seed(int(test_seed))  # TODO: not supported yet (also requires `core_eval_env = gym.make(args.env)`)
+
+    # calculate corresponding `steps` and `eval_interval` according to frameskip
+    # 8,000,000 frames = 1333 episodes if we count an episode as 6000 frames,
+    # 8,000,000 frames = 1000 episodes if we count an episode as 8000 frames.
+    maximum_frames = 8000000
+    if args.frame_skip is None:
+        steps = maximum_frames
+        eval_interval = 6000 * 100  # (approx.) every 100 episode (counts "1 episode = 6000 steps")
+    else:
+        steps = maximum_frames // args.frame_skip
+        eval_interval = 6000 * 100 // args.frame_skip  # (approx.) every 100 episode (counts "1 episode = 6000 steps")
+
+    agent = get_agent(
+        n_actions=env.action_space.n, arch=args.arch, n_input_channels=env.observation_space.shape[0],
+        noisy_net_sigma=args.noisy_net_sigma, final_epsilon=args.final_epsilon,
+        final_exploration_frames=args.final_exploration_frames, explorer_sample_func=env.action_space.sample,
+        lr=args.lr, adam_eps=args.adam_eps,
+        prioritized=args.prioritized, steps=steps, update_interval=args.update_interval,
+        replay_capacity=args.replay_capacity, num_step_return=args.num_step_return,
+        agent_type=args.agent, gpu=args.gpu, gamma=args.gamma, replay_start_size=args.replay_start_size,
+        target_update_interval=args.target_update_interval, clip_delta=args.clip_delta,
+        batch_accumulator=args.batch_accumulator
+    )
+
+    if args.load:
+        agent.load(args.load)
+
+    # experiment
+    if args.demo:
+        eval_stats = pfrl.experiments.eval_performance(env=eval_env, agent=agent, n_steps=None, n_episodes=args.eval_n_runs)
+        logger.info('n_runs: {} mean: {} median: {} stdev {}'.format(
+            args.eval_n_runs, eval_stats['mean'], eval_stats['median'], eval_stats['stdev']))
+    else:
+        pfrl.experiments.train_agent_with_evaluation(
+            agent=agent, env=env, steps=steps,
+            eval_n_steps=None, eval_n_episodes=args.eval_n_runs, eval_interval=eval_interval,
+            outdir=args.outdir, eval_env=eval_env, save_best_so_far_agent=True,
+        )
+
+    env.close()
+    eval_env.close()
+
+
+def parse_agent(agent):
+    return {'DQN': pfrl.agents.DQN,
+            'DoubleDQN': pfrl.agents.DoubleDQN,
+            'PAL': pfrl.agents.PAL,
+            'CategoricalDoubleDQN': pfrl.agents.CategoricalDoubleDQN}[agent]
+
+
+def get_agent(
+        n_actions, arch, n_input_channels,
+        noisy_net_sigma, final_epsilon, final_exploration_frames, explorer_sample_func,
+        lr, adam_eps,
+        prioritized, steps, update_interval, replay_capacity, num_step_return,
+        agent_type, gpu, gamma, replay_start_size, target_update_interval, clip_delta, batch_accumulator
+):
+    # Q function
+    q_func = parse_arch(arch, n_actions, n_input_channels=n_input_channels)
+
+    # explorer
+    if noisy_net_sigma is not None:
+        pfrl.nn.to_factorized_noisy(q_func, sigma_scale=noisy_net_sigma)
+        # Turn off explorer
+        explorer = pfrl.explorers.Greedy()
+    else:
+        explorer = pfrl.explorers.LinearDecayEpsilonGreedy(
+            1.0, final_epsilon, final_exploration_frames, explorer_sample_func)
+
+    opt = torch.optim.Adam(q_func.parameters(), lr, eps=adam_eps)  # NOTE: mirrors DQN implementation in MineRL paper
+
+    # Select a replay buffer to use
+    if prioritized:
+        # Anneal beta from beta0 to 1 throughout training
+        betasteps = steps / update_interval
+        rbuf = pfrl.replay_buffers.PrioritizedReplayBuffer(
+            replay_capacity, alpha=0.5, beta0=0.4, betasteps=betasteps, num_steps=num_step_return)
+    else:
+        rbuf = pfrl.replay_buffers.ReplayBuffer(replay_capacity, num_step_return)
+
+    # build agent
+    def phi(x):
+        # observation -> NN input
+        return np.asarray(x)
+    Agent = parse_agent(agent_type)
+    agent = Agent(
+        q_func, opt, rbuf, gpu=gpu, gamma=gamma, explorer=explorer, replay_start_size=replay_start_size,
+        target_update_interval=target_update_interval, clip_delta=clip_delta, update_interval=update_interval,
+        batch_accumulator=batch_accumulator, phi=phi)
+
+    return agent
+
+
+if __name__ == '__main__':
+    main()

mod/env_wrappers.py

+import copy
+from logging import getLogger
+from collections import 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__)
+
+
+def wrap_env(
+        env, test,
+        env_id,
+        monitor, outdir,
+        frame_skip,
+        gray_scale, frame_stack,
+        randomize_action, eval_epsilon,
+        action_choices):
+    # 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)
+
+    # wrap env: observation...
+    # NOTE: wrapping order matters!
+
+    if test and monitor:
+        env = Monitor(
+            env, os.path.join(outdir, env.spec.id, 'monitor'),
+            mode='evaluation' if test else 'training', video_callable=lambda episode_id: True)
+    if frame_skip is not None:
+        env = FrameSkip(env, skip=frame_skip)
+    if gray_scale:
+        env = GrayScaleWrapper(env, dict_space_key='pov')
+    env = ObtainPoVWrapper(env)
+    env = MoveAxisWrapper(env, source=-1, destination=0)  # convert hwc -> chw as Pytorch requires.
+    env = ScaledFloatFrame(env)
+    if frame_stack is not None and frame_stack > 0:
+        env = FrameStack(env, frame_stack, channel_order='chw')
+
+    env = ClusteredActionWrapper(env, clusters=action_choices)
+
+    if randomize_action:
+        env = RandomizeAction(env, eval_epsilon)
+
+    return env
+
+
+class FrameSkip(gym.Wrapper):
+    """Return every `skip`-th frame and repeat given action during skip.
+
+    Note that this wrapper does not "maximize" over the skipped frames.
+    """
+    def __init__(self, env, skip=4):
+        super().__init__(env)
+
+        self._skip = skip
+
+    def step(self, action):
+        total_reward = 0.0
+        for _ in range(self._skip):
+            obs, reward, done, info = self.env.step(action)
+            total_reward += reward
+            if done:
+                break
+        return obs, total_reward, done, info
+
+
+class FrameStack(gym.Wrapper):
+    def __init__(self, env, k, channel_order='hwc', use_tuple=False):
+        """Stack k last frames.
+
+        Returns lazy array, which is much more memory efficient.
+        """
+        gym.Wrapper.__init__(self, env)
+        self.k = k
+        self.observations = deque([], maxlen=k)
+        self.stack_axis = {'hwc': 2, 'chw': 0}[channel_order]
+        self.use_tuple = use_tuple
+
+        if self.use_tuple:
+            pov_space = env.observation_space[0]
+            inv_space = env.observation_space[1]
+        else:
+            pov_space = env.observation_space
+
+        low_pov = np.repeat(pov_space.low, k, axis=self.stack_axis)
+        high_pov = np.repeat(pov_space.high, k, axis=self.stack_axis)
+        pov_space = gym.spaces.Box(low=low_pov, high=high_pov, dtype=pov_space.dtype)
+
+        if self.use_tuple:
+            low_inv = np.repeat(inv_space.low, k, axis=0)
+            high_inv = np.repeat(inv_space.high, k, axis=0)
+            inv_space = gym.spaces.Box(low=low_inv, high=high_inv, dtype=inv_space.dtype)
+            self.observation_space = gym.spaces.Tuple(
+                (pov_space, inv_space))
+        else:
+            self.observation_space = pov_space
+
+    def reset(self):
+        ob = self.env.reset()
+        for _ in range(self.k):
+            self.observations.append(ob)
+        return self._get_ob()
+
+    def step(self, action):
+        ob, reward, done, info = self.env.step(action)
+        self.observations.append(ob)
+        return self._get_ob(), reward, done, info
+
+    def _get_ob(self):
+        assert len(self.observations) == self.k
+        if self.use_tuple:
+            frames = [x[0] for x in self.observations]
+            inventory = [x[1] for x in self.observations]
+            return (LazyFrames(list(frames), stack_axis=self.stack_axis),
+                    LazyFrames(list(inventory), stack_axis=0))
+        else:
+            return LazyFrames(list(self.observations), stack_axis=self.stack_axis)
+
+
+class ObtainPoVWrapper(gym.ObservationWrapper):
+    """Obtain 'pov' value (current game display) of the original observation."""
+    def __init__(self, env):
+        super().__init__(env)
+
+        self.observation_space = self.env.observation_space.spaces['pov']
+
+    def observation(self, observation):
+        return observation['pov']
+
+
+class UnifiedObservationWrapper(gym.ObservationWrapper):
+    """Take 'pov', 'compassAngle', 'inventory' and concatenate with scaling.
+    Each element of 'inventory' is converted to a square whose side length is region_size.
+    The color of each square is correlated to the reciprocal of (the number of the corresponding item + 1).
+    """
+    def __init__(self, env, region_size=8):
+        super().__init__(env)
+
+        self._compass_angle_scale = 180 / 255  # NOTE: `ScaledFloatFrame` will scale the pixel values with 255.0 later
+        self.region_size = region_size
+
+        pov_space = self.env.observation_space.spaces['pov']
+        low_dict = {'pov': pov_space.low}
+        high_dict = {'pov': pov_space.high}
+
+        if 'compassAngle' in self.env.observation_space.spaces:
+            compass_angle_space = self.env.observation_space.spaces['compassAngle']
+            low_dict['compassAngle'] = compass_angle_space.low
+            high_dict['compassAngle'] = compass_angle_space.high
+
+        if 'inventory' in self.env.observation_space.spaces:
+            inventory_space = self.env.observation_space.spaces['inventory']
+            low_dict['inventory'] = {}
+            high_dict['inventory'] = {}
+            for key in inventory_space.spaces.keys():
+                low_dict['inventory'][key] = inventory_space.spaces[key].low
+                high_dict['inventory'][key] = inventory_space.spaces[key].high
+
+        low = self.observation(low_dict)
+        high = self.observation(high_dict)
+
+        self.observation_space = gym.spaces.Box(low=low, high=high)
+
+    def observation(self, observation):
+        obs = observation['pov']
+        pov_dtype = obs.dtype
+
+        if 'compassAngle' in observation:
+            compass_scaled = observation['compassAngle'] / self._compass_angle_scale
+            compass_channel = np.ones(shape=list(obs.shape[:-1]) + [1], dtype=pov_dtype) * compass_scaled
+            obs = np.concatenate([obs, compass_channel], axis=-1)
+        if 'inventory' in observation:
+            assert len(obs.shape[:-1]) == 2
+            region_max_height = obs.shape[0]
+            region_max_width = obs.shape[1]
+            rs = self.region_size
+            if min(region_max_height, region_max_width) < rs:
+                raise ValueError("'region_size' is too large.")
+            num_element_width = region_max_width // rs
+            inventory_channel = np.zeros(shape=list(obs.shape[:-1]) + [1], dtype=pov_dtype)
+            for idx, key in enumerate(observation['inventory']):
+                item_scaled = np.clip(255 - 255 / (observation['inventory'][key] + 1),  # Inversed
+                                      0, 255)
+                item_channel = np.ones(shape=[rs, rs, 1], dtype=pov_dtype) * item_scaled
+                width_low = (idx % num_element_width) * rs
+                height_low = (idx // num_element_width) * rs
+                if height_low + rs > region_max_height:
+                    raise ValueError("Too many elements on 'inventory'. Please decrease 'region_size' of each component")
+                inventory_channel[height_low:(height_low + rs), width_low:(width_low + rs), :] = item_channel
+            obs = np.concatenate([obs, inventory_channel], axis=-1)
+        return obs
+
+
+class FullObservationSpaceWrapper(gym.ObservationWrapper):
+    """Returns as observation a tuple with the frames and a list of
+    compassAngle and inventory items.
+    compassAngle is scaled to be in the interval [-1, 1] and inventory items
+    are scaled to be in the interval [0, 1]
+    """
+    def __init__(self, env):
+        super().__init__(env)
+
+        pov_space = self.env.observation_space.spaces['pov']
+
+        low_dict = {'pov': pov_space.low, 'inventory': {}}
+        high_dict = {'pov': pov_space.high, 'inventory': {}}
+
+        for obs_name in self.env.observation_space.spaces['inventory'].spaces.keys():
+            obs_space = self.env.observation_space.spaces['inventory'].spaces[obs_name]
+            low_dict['inventory'][obs_name] = obs_space.low
+            high_dict['inventory'][obs_name] = obs_space.high
+
+        if 'compassAngle' in self.env.observation_space.spaces:
+            compass_angle_space = self.env.observation_space.spaces['compassAngle']
+            low_dict['compassAngle'] = compass_angle_space.low
+            high_dict['compassAngle'] = compass_angle_space.high
+
+        low = self.observation(low_dict)
+        high = self.observation(high_dict)
+
+        pov_space = gym.spaces.Box(low=low[0], high=high[0])
+        inventory_space = gym.spaces.Box(low=low[1], high=high[1])
+        self.observation_space = gym.spaces.Tuple((pov_space, inventory_space))
+
+    def observation(self, observation):
+        frame = observation['pov']
+        inventory = []
+
+        if 'compassAngle' in observation:
+            compass_scaled = observation['compassAngle'] / 180
+            inventory.append(compass_scaled)
+
+        for obs_name in observation['inventory'].keys():
+            inventory.append(observation['inventory'][obs_name] / 2304)
+
+        inventory = np.array(inventory)
+        return (frame, inventory)
+
+
+class MoveAxisWrapper(gym.ObservationWrapper):
+    """Move axes of observation ndarrays."""
+    def __init__(self, env, source, destination, use_tuple=False):
+        if use_tuple:
+            assert isinstance(env.observation_space[0], gym.spaces.Box)
+        else:
+            assert isinstance(env.observation_space, gym.spaces.Box)
+        super().__init__(env)
+
+        self.source = source
+        self.destination = destination
+        self.use_tuple = use_tuple
+
+        if self.use_tuple:
+            low = self.observation(
+                tuple([space.low for space in self.observation_space]))
+            high = self.observation(
+                tuple([space.high for space in self.observation_space]))
+            dtype = self.observation_space[0].dtype
+            pov_space = gym.spaces.Box(low=low[0], high=high[0], dtype=dtype)
+            inventory_space = self.observation_space[1]
+            self.observation_space = gym.spaces.Tuple(
+                (pov_space, inventory_space))
+        else:
+            low = self.observation(self.observation_space.low)
+            high = self.observation(self.observation_space.high)
+            dtype = self.observation_space.dtype
+            self.observation_space = gym.spaces.Box(