garage.envs.mujoco.half_cheetah_dir_env module

Variant of the HalfCheetahEnv with different target directions.

class HalfCheetahDirEnv(task=None)

Bases: garage.envs.mujoco.half_cheetah_env_meta_base.HalfCheetahEnvMetaBase

Half-cheetah environment with target direction, as described in [1].

The code is adapted from https://github.com/cbfinn/maml_rl/blob/9c8e2ebd741cb0c7b8bf2d040c4caeeb8e06cc95/rllab/envs/mujoco/half_cheetah_env_rand_direc.py

The half-cheetah follows the dynamics from MuJoCo [2], and receives at each time step a reward composed of a control cost and a reward equal to its velocity in the target direction. The tasks are generated by sampling the target directions from a Bernoulli distribution on {-1, 1} with parameter 0.5 (-1: backward, +1: forward).

[1] Chelsea Finn, Pieter Abbeel, Sergey Levine, “Model-Agnostic

Meta-Learning for Fast Adaptation of Deep Networks”, 2017 (https://arxiv.org/abs/1703.03400)

[2] Emanuel Todorov, Tom Erez, Yuval Tassa, “MuJoCo: A physics engine for

model-based control”, 2012 (https://homes.cs.washington.edu/~todorov/papers/TodorovIROS12.pdf)

Parameters:task (dict or None) – direction (float): Target direction, either -1 or 1.

sample_tasks(num_tasks)

Sample a list of num_tasks tasks.

Parameters:num_tasks (int) – Number of tasks to sample. Returns:

A list of “tasks,” where each task is a

dictionary containing a single key, “direction”, mapping to -1 or 1.

Return type:list[dict[str, float]]

set_task(task)

Reset with a task.

Parameters:task (dict[str, float]) – A task (a dictionary containing a single key, “direction”, mapping to -1 or 1).

step(action)

Take one step in the environment.

Equivalent to step in HalfCheetahEnv, but with different rewards.

Parameters:action (np.ndarray) – The action to take in the environment. Returns:

• observation (np.ndarray): The observation of the environment.
• reward (float): The reward acquired at this time step.

• done (boolean): Whether the environment was completed at this

  time step. Always False for this environment.

• infos (dict):

  • reward_forward (float): Reward for moving, ignoring the

    control cost.

  • reward_ctrl (float): The reward for acting i.e. the

    control cost (always negative).

  • task_dir (float): Target direction. 1.0 for forwards,

    -1.0 for backwards.

Return type:tuple