Module moog_demos.example_configs.first_person_predators_prey
First-person predator-prey task in an infinite plane.
In an inifite plane, predators and prey drift around and the agent is rewarded for catching prey and punished for being caught by predators. The field of view travels with the agent, keeping the agent centered at all times (first-person), and there is an occluding annulus around the agent.
The subject receives positive reward proportional to a prey's size when a prey is caught, and negative reward proportional to the predator's size when the agent is caught by a predator.
Functions
def get_config(_)-
Expand source code
def get_config(_): """Get environment config.""" ############################################################################ # Sprite initialization ############################################################################ # Agent agent = sprite.Sprite( x=0.5, y=0.5, shape='circle', scale=0.04, c0=0.33, c1=1., c2=0.66) annulus_vertices = shapes.annulus_vertices( inner_radius=0.08, outer_radius=0.3) agent_annulus = sprite.Sprite( x=0.5, y=0.5, shape=annulus_vertices, scale=1., c0=0.6, c1=1., c2=1.) # Predator generator max_predator_vel = 0.02 predator_pos = _get_boundary_pos_distribution(_FIELD_BUFFER) predator_vel = _get_vel_distribution( 0.5 * max_predator_vel, max_predator_vel) predator_factors = distribs.Product( [predator_pos, predator_vel, distribs.Continuous('scale', 0.07, 0.13)], shape='circle', c0=0., c1=1., c2=0.8, ) # Prey generator max_prey_vel = 0.01 prey_pos = _get_boundary_pos_distribution(_FIELD_BUFFER) prey_vel = _get_vel_distribution(0.5 * max_prey_vel, max_prey_vel) prey_factors = distribs.Product( [prey_pos, prey_vel, distribs.Continuous('scale', 0.07, 0.13)], shape='circle', c0=0.2, c1=1., c2=1., ) # Grid grid = shapes.grid_lines( grid_x=_GRID_SIZE, grid_y=_GRID_SIZE, buffer_border=1., c0=0., c1=0., c2=0.5) def state_initializer(): state = collections.OrderedDict([ ('grid', grid), ('prey', []), ('agent', [agent]), ('predators', []), ('agent_annulus', [agent_annulus]), ]) return state ############################################################################ # Physics ############################################################################ agent_friction_force = physics_lib.Drag(coeff_friction=0.25) physics = physics_lib.Physics( (agent_friction_force, ['agent', 'agent_annulus']), updates_per_env_step=10, ) ############################################################################ # Task ############################################################################ def _predator_reward_fn(_, predator_sprite): return -2. * predator_sprite.scale predator_task = tasks.ContactReward( reward_fn=_predator_reward_fn, layers_0='agent', layers_1='predators', reset_steps_after_contact=0, ) def _prey_reward_fn(_, prey_sprite): return prey_sprite.scale prey_task = tasks.ContactReward( reward_fn=_prey_reward_fn, layers_0='agent', layers_1='prey', ) task = tasks.CompositeTask(predator_task, prey_task) ############################################################################ # Action space ############################################################################ action_space = action_spaces.Joystick( scaling_factor=0.003, action_layers=('agent', 'agent_annulus'), constrained_lr=False, ) ############################################################################ # Observer ############################################################################ _polygon_modifier = observers.polygon_modifiers.FirstPersonAgent( agent_layer='agent') observer = observers.PILRenderer( image_size=(64, 64), anti_aliasing=1, color_to_rgb='hsv_to_rgb', polygon_modifier=_polygon_modifier, ) ############################################################################ # Game rules ############################################################################ # Make predators appear randomly predator_appear_generator = sprite_generators.generate_sprites( predator_factors, num_sprites=1) predator_appear = game_rules.ConditionalRule( condition=lambda state: np.random.binomial(1, p=0.5), rules=game_rules.CreateSprites('predators', predator_appear_generator), ) # Make prey appear randomly prey_appear_generator = sprite_generators.generate_sprites( prey_factors, num_sprites=1) prey_appear = game_rules.ConditionalRule( condition=lambda state: np.random.binomial(1, p=0.2), rules=game_rules.CreateSprites('prey', prey_appear_generator), ) # Make predators and prey vanish when they are distant enough and moving # away. vanish_range = [-1. * _VANISH_DIST, 1. + _VANISH_DIST] def _should_vanish(s): pos_too_small = (s.position < vanish_range[0]) * (s.velocity < 0.) pos_too_large = (s.position > vanish_range[1]) * (s.velocity > 0.) return any(pos_too_small) or any(pos_too_large) predator_vanish = game_rules.VanishByFilter('predators', _should_vanish) prey_vanish = game_rules.VanishByFilter('prey', _should_vanish) # Keep agent near center keep_near_center = game_rules.KeepNearCenter( agent_layer='agent', layers_to_center=['agent_annulus', 'predators', 'prey'], grid_x=_GRID_SIZE, ) # Make prey vanish when contacted by agent prey_caught = game_rules.VanishOnContact( vanishing_layer='prey', contacting_layer='agent') rules = (predator_appear, prey_appear, prey_vanish, predator_vanish, keep_near_center, prey_caught) ############################################################################ # Final config ############################################################################ config = { 'state_initializer': state_initializer, 'physics': physics, 'task': task, 'action_space': action_space, 'observers': {'image': observer}, 'game_rules': rules, } return configGet environment config.