def training_workflow(config, reporter):
from gym.spaces import Box
import numpy as np
env_maker = get_env_maker(GazeboEnv)
env, agents = env_maker(config['env_config'], return_agents=True)
space = Box(low=-np.ones(2), high=np.ones(2))
# pdb.set_trace()
replay_buffers = {
agent_id: ReplayBuffer(config.get('buffer_size', 1000))
for agent_id in agents
}
policy = {
k: (RandomPolicy, a.observation_space, a.action_space, {})
for k, a in agents.items()
}
worker = RolloutWorker(lambda x: env,
policy=policy,
batch_steps=32,
policy_mapping_fn=lambda x: x,
episode_horizon=20)
for i in range(config['num_iters']):
T1 = SampleBatch.concat_samples([worker.sample()])
for agent_id, batch in T1.policy_batches.items():
for row in batch.rows():
replay_buffers[agent_id].add(row['obs'],
row['actions'],
row['rewards'],
row['new_obs'],
row['dones'],
weight=None)
pdb.set_trace()
get_dock_marks = []
workers = RolloutWorker(env_creator,
ppo.PPOTFPolicy,
env_config=envconf,
policy_config=d)
with open(checkpoint, 'rb') as c:
c = c.read()
c = pickle.loads(c)
print(list(c.keys()))
workers.restore(c['worker'])
fp_path = "/Users/austin/PycharmProjects/RLDock/"
with open("log.pml", 'w') as fp:
with open("test.pml", 'w') as f:
for j in range(1):
rs = workers.sample()
print(rs)
print(list(rs.keys()))
ls = rs['actions'].shape[0]
for i in range(ls):
i += j * ls
ligand_pdb = rs['infos'][i - j * ls]['atom']
protein_pdb_link = rs['infos'][i - j * ls]['protein']
with open(fp_path + 'pdbs_traj/test' + str(i) + '.pdb',
'w') as f:
f.write(ligand_pdb)
shutil.copyfile(
protein_pdb_link,
fp_path + 'pdbs_traj/test_p' + str(i) + '.pdb')