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Active-Gym

Active-Gym is a collection of environments for Active Vision Reinforcement Learning. It builds on top of existing common visual RL environments including Atari, DeepMind Control suite, and Robosuite. All environments are compatible with Gymasium interface.

What is Active(Vision)-RL? It is a process that allows an embodied agent to actively gather new information in an environment. This library particularly focuses on actively managing visual observations, for example an image crop in a 2D environment or a viewpoint in a 3D environment. The agent takes motor actions (like manipulations, physical interactions) and sensory actions (like eye movements) together to finish a task.

Change Logs

  • 2023/12/27: Added support for RLBench. Version number 0.1 -> 0.2.
    • TODO: may need to fix camera movement in RLBench
  • 2023/08: Added support for Robosuite.

Installation

pip install git+https://github.com/elicassion/active-gym.git

or clone and install it in editable mode

git clone https://github.com/elicassion/active-gym.git
cd active-gym
pip install -e .

If you are interested in using robot environments (like Robosuite), you can do

pip install git+https://github.com/elicassion/active-gym.git[robosuite]

or follow the installation of Robosuite

Quick Start

Atari

from active_gym import AtariBaseEnv, AtariEnvArgs
env_args = AtariEnvArgs(
    game=env_name, 
    seed=seed, 
    obs_size=(84, 84), 
    fov_size=(30, 30),               # the partial observation size
    fov_init_loc=(0, 0),             # the initial partial observation location
    sensory_action_mode="absolute",  # change the observation location by abs coordinates
    record=False,                    # it integrates recording, if needed
)
env = AtariFixedFovealEnv(env_args)
obs, info = env.reset()
for i in range(5):
    obs, reward, done, truncated, info = env.step(env.action_space.sample())
    # action is in the form of gymnasium.spaces.Dict
    # {"motor_action": np.ndarray, "sensory_action": np.ndarray}

Robosuite

from active_gym import make_active_robosuite_env, RobosuiteEnvArgs
from robosuite.controllers import load_controller_config
env_args = RobosuiteEnvArgs(
    task=task_name,                 # task name like "Lift"
    seed=seed,                      # random seed
    obs_size=(84, 84),              # observation resolution
    robots=["Panda", "Panda"] if "TwoArm" in task_name else "Panda",              # robot config
    controller_configs = load_controller_config(default_controller="OSC_POSE")    # optionally change the controller
    camera_names=["sideview", "active_view", "agentview"],                        # views available in simulation
    selected_obs_names=["sideview_image", "active_view_image", "agentview_image"] # selected observation returned in observation, must be a subset of <camera_names>
    init_view="sideview",           # where to initialize the active camera, the selected one should be presented in <camera_names>
    **kwargs
)
env = make_active_robosuite_env(env_args)
obs, info = env.reset()
# obs["active_view_image"]          # the image observation from active_view
for i in range(5):
    obs, reward, done, truncated, info = env.step(env.action_space.sample())
    # action is in the form of gymnasium.spaces.Dict
    # {"motor_action": np.ndarray, "sensory_action": np.ndarray}

More examples can be found at if __name__ == "__main__" part in atari_env.py, dmc_env.py, and robosuite_env.py.

Supported Types of Environments

  • Atari
AtariBaseEnv                        # Base environment with full observation
AtariFixedFovealEnv                 # Active environment with limited foveal visual observation, at fixed size
AtariFlexibleFovealEnv              # Active environment with options to change foveal observation size
AtariFixedFovealPeripheralEnv       # Active environment with both foveal and peripheral observations
  • DMC
DMCBaseEnv
DMCFixedFovealEnv
DMCFlexibleFovealEnv
DMCFixedFovealPeripheralEnv
  • Robosuite
# All robosuite environments are supported by adding a movable camera.
from active_gym import make_active_robosuite_env
make_active_robosuite_env(task="<robosuite_env_name>", <more_args>)
  • RLBench
make_active_rlbench_env(task="<rlbench_env_name>", <more_args>)

Note: frame stacking and action repeat are intergrated in Atari and DMC environments.

TODO List

All suggestions on bug-fix, new features, and improvmets are welcomed. Please open an issue or contact jishang[at]cs.stonybrook.edu

  • Issac Gym
  • RLBench
  • Robosuite

Citation

Please consider cite us if you find this repo helpful.

@article{shang2023active,
    title={Active Reinforcement Learning under Limited Visual Observability},
    author={Jinghuan Shang and Michael S. Ryoo},
    journal={arXiv preprint},
    year={2023},
    eprint={2306.00975},
}

Acknowledgement

We thank the implementation of the base environments used in this repo: Atari, DeepMind Control suite, Robosuite, and dmc2gym.