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| import numpy as np | |
| from gym import utils | |
| from gym.envs.mujoco import mujoco_env | |
| import os | |
| from jinja2 import Template | |
| class ManyAgentSwimmerEnv(mujoco_env.MujocoEnv, utils.EzPickle): | |
| def __init__(self, **kwargs): | |
| agent_conf = kwargs.get("agent_conf") | |
| n_agents = int(agent_conf.split("x")[0]) | |
| n_segs_per_agents = int(agent_conf.split("x")[1]) | |
| n_segs = n_agents * n_segs_per_agents | |
| # Check whether asset file exists already, otherwise create it | |
| asset_path = os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), 'assets', | |
| 'manyagent_swimmer_{}_agents_each_{}_segments.auto.xml'.format(n_agents, n_segs_per_agents) | |
| ) | |
| # if not os.path.exists(asset_path): | |
| print("Auto-Generating Manyagent Swimmer asset with {} segments at {}.".format(n_segs, asset_path)) | |
| self._generate_asset(n_segs=n_segs, asset_path=asset_path) | |
| #asset_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'assets',git p | |
| # 'manyagent_swimmer.xml') | |
| mujoco_env.MujocoEnv.__init__(self, asset_path, 4) | |
| utils.EzPickle.__init__(self) | |
| def _generate_asset(self, n_segs, asset_path): | |
| template_path = os.path.join( | |
| os.path.dirname(os.path.abspath(__file__)), 'assets', 'manyagent_swimmer.xml.template' | |
| ) | |
| with open(template_path, "r") as f: | |
| t = Template(f.read()) | |
| body_str_template = """ | |
| <body name="mid{:d}" pos="-1 0 0"> | |
| <geom density="1000" fromto="0 0 0 -1 0 0" size="0.1" type="capsule"/> | |
| <joint axis="0 0 {:d}" limited="true" name="rot{:d}" pos="0 0 0" range="-100 100" type="hinge"/> | |
| """ | |
| body_end_str_template = """ | |
| <body name="back" pos="-1 0 0"> | |
| <geom density="1000" fromto="0 0 0 -1 0 0" size="0.1" type="capsule"/> | |
| <joint axis="0 0 1" limited="true" name="rot{:d}" pos="0 0 0" range="-100 100" type="hinge"/> | |
| </body> | |
| """ | |
| body_close_str_template = "</body>\n" | |
| actuator_str_template = """\t <motor ctrllimited="true" ctrlrange="-1 1" gear="150.0" joint="rot{:d}"/>\n""" | |
| body_str = "" | |
| for i in range(1, n_segs - 1): | |
| body_str += body_str_template.format(i, (-1) ** (i + 1), i) | |
| body_str += body_end_str_template.format(n_segs - 1) | |
| body_str += body_close_str_template * (n_segs - 2) | |
| actuator_str = "" | |
| for i in range(n_segs): | |
| actuator_str += actuator_str_template.format(i) | |
| rt = t.render(body=body_str, actuators=actuator_str) | |
| with open(asset_path, "w") as f: | |
| f.write(rt) | |
| pass | |
| def step(self, a): | |
| ctrl_cost_coeff = 0.0001 | |
| xposbefore = self.sim.data.qpos[0] | |
| self.do_simulation(a, self.frame_skip) | |
| xposafter = self.sim.data.qpos[0] | |
| reward_fwd = (xposafter - xposbefore) / self.dt | |
| reward_ctrl = -ctrl_cost_coeff * np.square(a).sum() | |
| reward = reward_fwd + reward_ctrl | |
| ob = self._get_obs() | |
| return ob, reward, False, dict(reward_fwd=reward_fwd, reward_ctrl=reward_ctrl) | |
| def _get_obs(self): | |
| qpos = self.sim.data.qpos | |
| qvel = self.sim.data.qvel | |
| return np.concatenate([qpos.flat[2:], qvel.flat]) | |
| def reset_model(self): | |
| self.set_state( | |
| self.init_qpos + self.np_random.uniform(low=-.1, high=.1, size=self.model.nq), | |
| self.init_qvel + self.np_random.uniform(low=-.1, high=.1, size=self.model.nv) | |
| ) | |
| return self._get_obs() | |