rofunc.learning.RofuncRL.tasks.isaacgymenv.ant

1.  Module Contents

1.1.  Classes

AntTask

1.2.  Functions

compute_ant_reward
compute_ant_observations

1.3.  API

class rofunc.learning.RofuncRL.tasks.isaacgymenv.ant.AntTask(cfg, rl_device, sim_device, graphics_device_id, headless, virtual_screen_capture, force_render)

Bases: rofunc.learning.RofuncRL.tasks.isaacgymenv.base.vec_task.VecTask

create_sim()

compute_reward(actions)

compute_observations()

reset_idx(env_ids)

pre_physics_step(actions)

post_physics_step()

rofunc.learning.RofuncRL.tasks.isaacgymenv.ant.compute_ant_reward(obs_buf: Tensor, reset_buf: Tensor, progress_buf: Tensor, actions: Tensor, up_weight: float, heading_weight: float, potentials: Tensor, prev_potentials: Tensor, actions_cost_scale: float, energy_cost_scale: float, joints_at_limit_cost_scale: float, termination_height: float, death_cost: float, max_episode_length: float) → Tuple[Tensor, Tensor]

rofunc.learning.RofuncRL.tasks.isaacgymenv.ant.compute_ant_observations(obs_buf: Tensor, root_states: Tensor, targets: Tensor, potentials: Tensor, inv_start_rot: Tensor, dof_pos: Tensor, dof_vel: Tensor, dof_limits_lower: Tensor, dof_limits_upper: Tensor, dof_vel_scale: float, sensor_force_torques: Tensor, actions: Tensor, dt: float, contact_force_scale: float, basis_vec0: Tensor, basis_vec1: Tensor, up_axis_idx: int) → Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]