def test_learning(self):
pol_net = PolNet(self.env.observation_space,
self.env.action_space,
h1=32,
h2=32)
pol = GaussianPol(self.env.observation_space, self.env.action_space,
pol_net)
vf_net = VNet(self.env.observation_space)
vf = DeterministicSVfunc(self.env.observation_space, vf_net)
discrim_net = DiscrimNet(self.env.observation_space,
self.env.action_space,
h1=32,
h2=32)
discrim = DeterministicSAVfunc(self.env.observation_space,
self.env.action_space, discrim_net)
sampler = EpiSampler(self.env, pol, num_parallel=1)
optim_vf = torch.optim.Adam(vf_net.parameters(), 3e-4)
optim_discrim = torch.optim.Adam(discrim_net.parameters(), 3e-4)
with open(os.path.join('data/expert_epis', 'Pendulum-v0_2epis.pkl'),
'rb') as f:
expert_epis = pickle.load(f)
expert_traj = Traj()
expert_traj.add_epis(expert_epis)
expert_traj.register_epis()
epis = sampler.sample(pol, max_steps=32)
agent_traj = Traj()
agent_traj.add_epis(epis)
agent_traj = ef.compute_pseudo_rews(agent_traj, discrim)
agent_traj = ef.compute_vs(agent_traj, vf)
agent_traj = ef.compute_rets(agent_traj, 0.99)
agent_traj = ef.compute_advs(agent_traj, 0.99, 0.95)
agent_traj = ef.centerize_advs(agent_traj)
agent_traj = ef.compute_h_masks(agent_traj)
agent_traj.register_epis()
result_dict = gail.train(agent_traj,
expert_traj,
pol,
vf,
discrim,
optim_vf,
optim_discrim,
rl_type='trpo',
epoch=1,
batch_size=32,
discrim_batch_size=32,
discrim_step=1,
pol_ent_beta=1e-3,
discrim_ent_beta=1e-5)
del sampler
parallel_dim=1 if args.rnn else 0)
discrim_net = DiscrimNet(ob_space,
ac_space,
h1=args.discrim_h1,
h2=args.discrim_h2)
discrim = DeterministicSAVfunc(ob_space,
ac_space,
discrim_net,
data_parallel=args.data_parallel)
sampler = EpiSampler(env, pol, num_parallel=args.num_parallel, seed=args.seed)
optim_pol = torch.optim.Adam(pol_net.parameters(), args.pol_lr)
optim_vf = torch.optim.Adam(vf_net.parameters(), args.vf_lr)
optim_discrim = torch.optim.Adam(discrim_net.parameters(), args.discrim_lr)
with open(os.path.join(args.expert_dir, args.expert_fname), 'rb') as f:
expert_epis = pickle.load(f)
expert_traj = Traj()
expert_traj.add_epis(expert_epis)
expert_traj.register_epis()
expert_rewards = [np.sum(epi['rews']) for epi in expert_epis]
expert_mean_rew = np.mean(expert_rewards)
logger.log('expert_score={}'.format(expert_mean_rew))
logger.log('expert_num_epi={}'.format(expert_traj.num_epi))
total_epi = 0
total_step = 0
max_rew = -1e6