randomized cup
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Onur
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@ -213,16 +213,26 @@ register(id='TableTennis4DCtxt-v0',
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kwargs={'ctxt_dim': 4})
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## BeerPong
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difficulties = ["simple", "intermediate", "hard", "hardest"]
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for v, difficulty in enumerate(difficulties):
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register(
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id='ALRBeerPong-v{}'.format(v),
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# fixed goal cup position
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register(
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id='ALRBeerPong-v0',
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entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
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max_episode_steps=600,
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kwargs={
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"difficulty": difficulty,
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"reward_type": "staged",
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"rndm_goal": False,
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"cup_goal_pos": [-0.3, -1.2]
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}
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)
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# random goal cup position
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register(
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id='ALRBeerPong-v1',
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entry_point='alr_envs.alr.mujoco:ALRBeerBongEnv',
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max_episode_steps=600,
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kwargs={
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"rndm_goal": True,
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"cup_goal_pos": [-0.3, -1.2]
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}
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)
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@ -4,11 +4,16 @@ import os
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import numpy as np
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from gym import utils
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from gym.envs.mujoco import MujocoEnv
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from alr_envs.alr.mujoco.beerpong.beerpong_reward_staged import BeerPongReward
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CUP_POS_MIN = np.array([-0.32, -2.2])
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CUP_POS_MAX = np.array([0.32, -1.2])
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class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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def __init__(self, frame_skip=1, apply_gravity_comp=True, reward_type: str = "staged", noisy=False,
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context: np.ndarray = None, difficulty='simple'):
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def __init__(self, frame_skip=1, apply_gravity_comp=True, noisy=False,
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rndm_goal=False, cup_goal_pos=[-0.3, -1.2]):
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self._steps = 0
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self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
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@ -17,7 +22,7 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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self.j_min = np.array([-2.6, -1.985, -2.8, -0.9, -4.55, -1.5707, -2.7])
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self.j_max = np.array([2.6, 1.985, 2.8, 3.14159, 1.25, 1.5707, 2.7])
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self.context = context
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self.rndm_goal = rndm_goal
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self.apply_gravity_comp = apply_gravity_comp
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self.add_noise = noisy
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@ -38,23 +43,8 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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else:
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self.noise_std = 0
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if difficulty == 'simple':
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self.cup_goal_pos = np.array([0, -1.7, 0.840])
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elif difficulty == 'intermediate':
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self.cup_goal_pos = np.array([0.3, -1.5, 0.840])
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elif difficulty == 'hard':
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self.cup_goal_pos = np.array([-0.3, -2.2, 0.840])
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elif difficulty == 'hardest':
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self.cup_goal_pos = np.array([-0.3, -1.2, 0.840])
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if reward_type == "no_context":
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from alr_envs.alr.mujoco.beerpong.beerpong_reward import BeerPongReward
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reward_function = BeerPongReward
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elif reward_type == "staged":
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from alr_envs.alr.mujoco.beerpong.beerpong_reward_staged import BeerPongReward
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reward_function = BeerPongReward
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else:
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raise ValueError("Unknown reward type: {}".format(reward_type))
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self.cup_goal_pos = np.array(cup_goal_pos.append(0.840))
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reward_function = BeerPongReward
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self.reward_function = reward_function()
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MujocoEnv.__init__(self, self.xml_path, frame_skip)
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@ -94,6 +84,12 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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self.sim.model.body_pos[self.cup_table_id] = self.cup_goal_pos
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start_pos[7::] = self.sim.data.site_xpos[self.ball_site_id, :].copy()
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self.set_state(start_pos, init_vel)
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if self.rndm_goal:
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xy = np.random.uniform(CUP_POS_MIN, CUP_POS_MAX)
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xyz = np.zeros(3)
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xyz[:2] = xy
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xyz[-1] = 0.840
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self.sim.model.body_pos[self.cup_table_id] = xyz
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return self._get_obs()
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def step(self, a):
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@ -153,13 +149,12 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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def check_traj_in_joint_limits(self):
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return any(self.current_pos > self.j_max) or any(self.current_pos < self.j_min)
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# TODO: extend observation space
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:7]
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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# self.get_body_com("target"), # only return target to make problem harder
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self.sim.model.body_pos[self.cup_table_id][:2].copy(),
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[self._steps],
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])
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@ -169,25 +164,26 @@ class ALRBeerBongEnv(MujocoEnv, utils.EzPickle):
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return np.hstack([
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[False] * 7, # cos
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[False] * 7, # sin
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# [True] * 2, # x-y coordinates of target distance
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[True] * 2, # xy position of cup
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[False] # env steps
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])
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if __name__ == "__main__":
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env = ALRBeerBongEnv(reward_type="staged", difficulty='hardest')
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# env.configure(ctxt)
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env = ALRBeerBongEnv(rndm_goal=True)
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import time
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env.reset()
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env.render("human")
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for i in range(800):
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ac = 10 * env.action_space.sample()[0:7]
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for i in range(1500):
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# ac = 10 * env.action_space.sample()[0:7]
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ac = np.zeros(7)
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obs, rew, d, info = env.step(ac)
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env.render("human")
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print(rew)
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if d:
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break
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print('RESETTING')
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env.reset()
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time.sleep(1)
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env.close()
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@ -9,11 +9,10 @@ class MPWrapper(MPEnvWrapper):
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@property
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def active_obs(self):
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# TODO: @Max Filter observations correctly
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return np.hstack([
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[False] * 7, # cos
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[False] * 7, # sin
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# [True] * 2, # x-y coordinates of target distance
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[True] * 2, # xy position of cup
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[False] # env steps
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])
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@ -31,7 +30,6 @@ class MPWrapper(MPEnvWrapper):
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@property
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def goal_pos(self):
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# TODO: @Max I think the default value of returning to the start is reasonable here
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raise ValueError("Goal position is not available and has to be learnt based on the environment.")
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@property
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