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enable deepmimic training on mac

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This commit is contained in:
Erwin Coumans
2019-02-01 16:24:48 -08:00
parent 63e781b88b
commit 7cc2685faf
5 changed files with 21 additions and 12 deletions
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2
examples/pybullet/gym/pybullet_envs/deep_mimic/env/pd_controller_stable.py vendored
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@@ -141,4 +141,4 @@ class PDControllerStable(object):
maxF = np.array(maxForces) maxF = np.array(maxForces)
forces = np.clip(tau, -maxF , maxF ) forces = np.clip(tau, -maxF , maxF )
#print("c=",c) #print("c=",c)
return tau return tau

12
examples/pybullet/gym/pybullet_envs/deep_mimic/env/pybullet_deep_mimic_env.py vendored
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@@ -22,8 +22,10 @@ class PyBulletDeepMimicEnv(Env):
def reset(self): def reset(self):
self.t = 0 self.t = 0
if not self._isInitialized: if not self._isInitialized:
self._pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI) if self.enable_draw:
#self._pybullet_client = bullet_client.BulletClient() self._pybullet_client = bullet_client.BulletClient(connection_mode=p1.GUI)
else:
self._pybullet_client = bullet_client.BulletClient()
self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath()) self._pybullet_client.setAdditionalSearchPath(pybullet_data.getDataPath())
z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0]) z2y = self._pybullet_client.getQuaternionFromEuler([-math.pi*0.5,0,0])
@@ -198,7 +200,7 @@ class PyBulletDeepMimicEnv(Env):
state = self._humanoid.getState() state = self._humanoid.getState()
state[1]=state[1]+0.008 state[1]=state[1]+0.008
#print("record_state=",state) #print("record_state=",state)
return state return np.array(state)
def record_goal(self, agent_id): def record_goal(self, agent_id):
@@ -241,11 +243,11 @@ class PyBulletDeepMimicEnv(Env):
def is_episode_end(self): def is_episode_end(self):
isEnded = self._humanoid.terminates() isEnded = self._humanoid.terminates()
#also check maximum time, 20 seconds (todo get from file) #also check maximum time, 20 seconds (todo get from file)
print("self.t=",self.t) #print("self.t=",self.t)
if (self.t>3): if (self.t>3):
isEnded = True isEnded = True
return isEnded return isEnded
def check_valid_episode(self): def check_valid_episode(self):
#could check if limbs exceed velocity threshold #could check if limbs exceed velocity threshold
return true return true

5
examples/pybullet/gym/pybullet_envs/deep_mimic/learning/agent_builder.py
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@@ -1,12 +1,13 @@
import json import json
import numpy as np import numpy as np
from learning.ppo_agent import PPOAgent from learning.ppo_agent import PPOAgent
import pybullet_data
AGENT_TYPE_KEY = "AgentType" AGENT_TYPE_KEY = "AgentType"
def build_agent(world, id, file): def build_agent(world, id, file):
agent = None agent = None
with open(file) as data_file: with open(pybullet_data.getDataPath()+"/"+file) as data_file:
json_data = json.load(data_file) json_data = json.load(data_file)
assert AGENT_TYPE_KEY in json_data assert AGENT_TYPE_KEY in json_data
@@ -17,4 +18,4 @@ def build_agent(world, id, file):
else: else:
assert False, 'Unsupported agent type: ' + agent_type assert False, 'Unsupported agent type: ' + agent_type
return agent return agent

4
examples/pybullet/gym/pybullet_envs/deep_mimic/learning/replay_buffer.py
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@@ -2,7 +2,7 @@ import numpy as np
import copy import copy
from pybullet_utils.logger import Logger from pybullet_utils.logger import Logger
import inspect as inspect import inspect as inspect
from env.env import Env from pybullet_envs.deep_mimic.env.env import Env
import pybullet_utils.math_util as MathUtil import pybullet_utils.math_util as MathUtil
class ReplayBuffer(object): class ReplayBuffer(object):
@@ -348,4 +348,4 @@ class SampleBuffer(object):
count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX) count0 = np.sum(self.idx_to_slot == MathUtil.INVALID_IDX)
count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX) count1 = np.sum(self.slot_to_idx == MathUtil.INVALID_IDX)
valid &= count0 == count1 valid &= count0 == count1
return valid return valid

10
examples/pybullet/gym/pybullet_envs/deep_mimic/testrl.py
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@@ -1,5 +1,11 @@
import os
import inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(os.path.dirname(currentdir))
os.sys.path.insert(0,parentdir)
print("parentdir=",parentdir)
import json import json
from learning.rl_world import RLWorld from pybullet_envs.deep_mimic.learning.rl_world import RLWorld
from learning.ppo_agent import PPOAgent from learning.ppo_agent import PPOAgent
import pybullet_data import pybullet_data
@@ -36,7 +42,7 @@ print("bodies=",bodies)
int_output_path = arg_parser.parse_string("int_output_path") int_output_path = arg_parser.parse_string("int_output_path")
print("int_output_path=",int_output_path) print("int_output_path=",int_output_path)
agent_files = arg_parser.parse_string("agent_files") agent_files = pybullet_data.getDataPath()+"/"+arg_parser.parse_string("agent_files")
AGENT_TYPE_KEY = "AgentType" AGENT_TYPE_KEY = "AgentType"