def generate_new_game():
yaml_file = 'mazebasev2/options/knowledge_planner/' + filename
#yaml_file = 'mazebasev2/options/knowledge_planner/length12task_distractor.yaml'
with open(yaml_file, 'r') as handle:
options = yaml.load(handle)
# Get sub opts
method_opt = options['method']
env_opt = options['env']
log_opt = options['logs']
# Set up the mazebase environment
knowledge_root = env_opt['knowledge_root']
world_knowledge_file = os.path.join('mazebasev2', knowledge_root, env_opt['world_knowledge']['train'])
with open(world_knowledge_file) as f:
world_knowledge = json.load(f)
# Make the world
map_size = (env_opt['state_rep']['w'], env_opt['state_rep']['w'], env_opt['state_rep']['h'], env_opt['state_rep']['h'])
all_games = [games.BasicKnowledgeGame(world_knowledge=world_knowledge, proposed_knowledge=[], options=env_opt, load_items=None, map_size=map_size)]
# Game wrapper
game = games.MazeGame(
all_games,
featurizer=featurizers.GridFeaturizer()
)
return game
def reset(self):
self.count = 0
# Game wrapper
self.game = games.MazeGame(self.all_games,
featurizer=featurizers.GridFeaturizer())
#get observation
config = self.game.observe()
grid_obs, side_info = config['observation']
inventory = self.game.game.inventory
goal = self.game.game.goal
print(goal)
obs = (grid_obs, inventory, goal)
state, inventory, goal = obs
states_embedding = get_grid_embedding(state, self.glove,
self.embed_size)
states_onehot = one_hot_grid(state, self.glove, self.embed_size)
goal = get_goal_embedding(goal, self.glove, self.embed_size)
inventory = get_inventory_embedding(inventory, self.glove,
self.embed_size)
counts = np.array([self.game.game.count])
self.state = np.concatenate(
(counts.flatten(), states_embedding.flatten(),
states_onehot.flatten(), goal.flatten(), inventory.flatten()))
#k_prev_words = np.array([[self.vocabulary.word2idx['<start>']]])
#top_k_scores = np.zeros((1, 1))
#print(top_k_scores)
#print(k_prev_words.flatten())
#print(top_k_scores.flatten())
#no language:
#self.state = np.concatenate((k_prev_words.flatten(), top_k_scores.flatten(), states_embedding.flatten(), states_onehot.flatten(), goal.flatten(), inventory.flatten()))
#temp_embedding = torch.Tensor(states_embedding).to(self.device)
#temp_onehot = torch.Tensor(states_onehot).to(self.device)
#temp_inv = torch.Tensor(inventory).to(self.device)
#temp_goal = torch.Tensor(goal).to(self.device)
# ADD LANGUAGE MODEL STUFF!!
#all_sampled_ids = self.lang_model.get_hidden_state(temp_embedding, temp_onehot, temp_inv, temp_goal, self.device, self.vocabulary)
#bow_ids = [sent + [len(self.vocabulary)] * (20 - len(sent)) for sent in all_sampled_ids]
#bow_ids = np.array(bow_ids)
#sampled_ids, hidden_state = self.lang_model.get_hidden_state(states_embedding, states_onehot, inventory, goal, self.device, self.vocabulary)
#sampled_ids = sampled_ids + [-1]*(32-len(sampled_ids))
#sampled_ids = np.array(sampled_ids)
#hidden_state = hidden_state.data.numpy()
#self.state = np.concatenate((bow_ids.flatten(), states_embedding.flatten(), states_onehot.flatten(), goal.flatten(), inventory.flatten()))
return self.state
def generate_new_game():
#pick a random yaml file:
#num = random.randint(1,4)
#num = 2
# if num == 1:
# yaml_file = 'mazebasev2/options/knowledge_planner/length45common.yaml'
# elif num == 2:
# yaml_file = 'mazebasev2/options/knowledge_planner/length12task.yaml'
# else:
#yaml_file = 'mazebasev2/options/knowledge_planner/length3task.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length1task.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length2task.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length45common.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length3task.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length12task.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/minimum_viable_planning.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/minimum_viable_rl.yaml'
yaml_file = 'mazebasev2/options/knowledge_planner/unseen_tasks.yaml'
#yaml_file = 'mazebasev2/options/knowledge_planner/length12task_distractor.yaml'
with open(yaml_file, 'r') as handle:
options = yaml.load(handle)
# Get sub opts
method_opt = options['method']
env_opt = options['env']
log_opt = options['logs']
# Set up the mazebase environment
knowledge_root = env_opt['knowledge_root']
world_knowledge_file = os.path.join('mazebasev2', knowledge_root, env_opt['world_knowledge']['train'])
with open(world_knowledge_file) as f:
world_knowledge = json.load(f)
# Make the world
map_size = (env_opt['state_rep']['w'], env_opt['state_rep']['w'], env_opt['state_rep']['h'], env_opt['state_rep']['h'])
all_games = [games.BasicKnowledgeGame(world_knowledge=world_knowledge, proposed_knowledge=[], options=env_opt, load_items=None, map_size=map_size)]
# Game wrapper
game = games.MazeGame(
all_games,
featurizer=featurizers.GridFeaturizer()
)
return game
def reset(self):
self.count = 0
self.queue = []
# Game wrapper
self.game = games.MazeGame(self.all_games,
featurizer=featurizers.GridFeaturizer())
#get observation
config = self.game.observe()
grid_obs, side_info = config['observation']
inventory = self.game.game.inventory
goal = self.game.game.goal
#print(goal)
obs = (grid_obs, inventory, goal)
state, inventory, goal = obs
states_embedding = get_grid_embedding(state, self.glove,
self.embed_size)
states_onehot = one_hot_grid(state, self.glove, self.embed_size)
goal = get_goal_embedding(goal, self.glove, self.embed_size)
inventory = get_inventory_embedding(inventory, self.glove,
self.embed_size)
counts = np.array([self.game.game.count])
self.state = np.concatenate(
(counts.flatten(), states_embedding.flatten(),
states_onehot.flatten(), goal.flatten(), inventory.flatten()))
#print(self.state.shape)
self.combined_states = np.concatenate(
(self.state, self.state, self.state))
return self.combined_states
def start_game():
json_data = request.get_json(force=True)
player1 = json_data["a"]
entrance_code = json_data["b"]
if (player1, entrance_code) in game_count:
if game_count[(player1, entrance_code)] == 2:
completed = True
# otherwise, they have already done one. give the short task.
yaml_file = 'mazebasev2/options/knowledge_planner/short_task.yaml'
game_count[(player1, entrance_code)] = 2
elif player1 not in users:
users[player1] = 1
game_count[(player1, entrance_code)] = 1
yaml_file = 'mazebasev2/options/knowledge_planner/short_task.yaml'
else:
#pick a long or short randomly and add to game_count accordingly.
if random.uniform(0, 1) > 0.4:
yaml_file = 'mazebasev2/options/knowledge_planner/long_task.yaml'
game_count[(player1, entrance_code)] = 2
else:
yaml_file = 'mazebasev2/options/knowledge_planner/short_task.yaml'
game_count[(player1, entrance_code)] = 1
# yaml_file = 'mazebasev2/options/knowledge_planner/long_task.yaml'
# game_count[(player1, entrance_code)] = 2
# if player1 not in users or random.uniform(0, 1) < 0.4: # or random change
# yaml_file = 'mazebasev2/options/knowledge_planner/short_task.yaml'
# users[player1] = 1
# game_count[(player1, entrance_code)] = 1
with open(yaml_file, 'r') as handle:
options = yaml.load(handle)
# Get sub opts
method_opt = options['method']
env_opt = options['env']
log_opt = options['logs']
# Set up the mazebase environment
knowledge_root = env_opt['knowledge_root']
world_knowledge_file = os.path.join('mazebasev2', knowledge_root,
env_opt['world_knowledge']['train'])
with open(world_knowledge_file) as f:
world_knowledge = json.load(f)
# Make the world
map_size = (env_opt['state_rep']['w'], env_opt['state_rep']['w'],
env_opt['state_rep']['h'], env_opt['state_rep']['h'])
all_games = [
games.BasicKnowledgeGame(world_knowledge=world_knowledge,
proposed_knowledge=[],
options=env_opt,
load_items=None,
map_size=map_size)
]
# Game wrapper
game = games.MazeGame(all_games, featurizer=featurizers.GridFeaturizer())
current_games[(player1, entrance_code)] = game
game_observe = game.observe()
game_observe["extra_items"] = game.game.extra_items
game_observe["inventory"] = game.game.inventory
game_observe["goal"] = game.game.goal
game_observe["recipe"] = game.game.recipe
add_to_file(player1, entrance_code, game_observe, '1', str(time.time()))
#add_to_db(player1, entrance_code, game, '1', 'NONE', str(time.time()))
temp = game.display()
symbol_map, color_map, agent_loc = create_symbol_color_maps(temp)
completed = False
return jsonify(result=symbol_map,
color=color_map,
goal=game.game.goal,
inventory=game.game.inventory,
recipe=game.game.recipe,
agent=agent_loc,
links=link_lookup,
already_complete=completed)