/
eval_pdvf.py
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/
eval_pdvf.py
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import os, random, sys
import numpy as np
import torch
import torch.optim as optim
import torch.nn.functional as F
from pdvf_networks import PDVF
from pdvf_arguments import get_args
from ppo.model import Policy
from ppo.envs import make_vec_envs
import env_utils
import pdvf_utils
import train_utils
import myant
import myswimmer
import myspaceship
def eval_pdvf():
'''
Evaluate the Policy-Dynamics Value Function.
'''
args = get_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.set_num_threads(1)
device = args.device
if device != 'cpu':
torch.cuda.empty_cache()
if args.cuda and torch.cuda.is_available() and args.cuda_deterministic:
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
env = make_vec_envs(args, device)
env.reset()
names = []
for e in range(args.num_envs):
for s in range(args.num_seeds):
names.append('ppo.{}.env{}.seed{}.pt'.format(args.env_name, e, s))
source_policy = []
for name in names:
actor_critic = Policy(
env.observation_space.shape,
env.action_space,
base_kwargs={'recurrent': False})
actor_critic.to(device)
model = os.path.join(args.save_dir, name)
actor_critic.load_state_dict(torch.load(model))
source_policy.append(actor_critic)
# Load the collected interaction episodes for each agent
policy_encoder, policy_decoder = pdvf_utils.load_policy_model(
args, env)
env_encoder = pdvf_utils.load_dynamics_model(
args, env)
value_net = PDVF(env.observation_space.shape[0], args.dynamics_embedding_dim, args.hidden_dim_pdvf,
args.policy_embedding_dim, device=device).to(device)
value_net.to(device)
path_to_pdvf = os.path.join(args.save_dir_pdvf, \
"pdvf-stage{}.{}.pt".format(args.stage, args.env_name))
value_net.load_state_dict(torch.load(path_to_pdvf)['state_dict'])
value_net.eval()
all_envs = [i for i in range(args.num_envs)]
train_policies = [i for i in range(int(3/4*args.num_envs))]
train_envs = [i for i in range(int(3/4*args.num_envs))]
eval_envs = [i for i in range(int(3/4*args.num_envs), args.num_envs)]
env_enc_input_size = env.observation_space.shape[0] + env.action_space.shape[0]
sizes = pdvf_utils.DotDict({'state_dim': env.observation_space.shape[0], \
'action_dim': env.action_space.shape[0], 'env_enc_input_size': \
env_enc_input_size, 'env_max_seq_length': args.max_num_steps * env_enc_input_size})
env_sampler = env_utils.EnvSamplerPDVF(env, source_policy, args)
all_mean_rewards = [[] for _ in range(args.num_envs)]
all_mean_unnorm_rewards = [[] for _ in range(args.num_envs)]
# Eval on Train Envs
train_rewards = {}
unnorm_train_rewards = {}
for ei in range(len(all_envs)):
train_rewards[ei] = []
unnorm_train_rewards[ei] = []
for ei in train_envs:
for i in range(args.num_eval_eps):
args.seed = i
np.random.seed(seed=i)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
for pi in train_policies:
init_obs = torch.FloatTensor(env_sampler.env.reset(env_id=ei))
if 'ant' in args.env_name or 'swimmer' in args.env_name:
init_state = env_sampler.env.sim.get_state()
res = env_sampler.zeroshot_sample_src_from_pol_state_mujoco(args, init_obs, sizes, policy_idx=pi, env_idx=ei)
else:
init_state = env_sampler.env.state
res = env_sampler.zeroshot_sample_src_from_pol_state(args, init_obs, sizes, policy_idx=pi, env_idx=ei)
source_env = res['source_env']
mask_env = res['mask_env']
source_policy = res['source_policy']
init_episode_reward = res['episode_reward']
mask_policy = res['mask_policy']
if source_policy.shape[1] == 1:
source_policy = source_policy.repeat(1, 2, 1)
mask_policy = mask_policy.repeat(1, 1, 2)
emb_policy = policy_encoder(source_policy.detach().to(device),
mask_policy.detach().to(device)).detach()
if source_env.shape[1] == 1:
source_env = source_env.repeat(1, 2, 1)
mask_env = mask_env.repeat(1, 1, 2)
emb_env = env_encoder(source_env.detach().to(device),
mask_env.detach().to(device)).detach()
emb_policy = F.normalize(emb_policy, p=2, dim=1).detach()
emb_env = F.normalize(emb_env, p=2, dim=1).detach()
pred_value = value_net(init_obs.unsqueeze(0).to(device),
emb_env.to(device), emb_policy.to(device)).item()
if 'ant' in args.env_name or 'swimmer' in args.env_name:
decoded_reward = env_sampler.get_reward_pol_embedding_state_mujoco(args,
init_state, init_obs, emb_policy, policy_decoder, env_idx=ei)[0]
else:
decoded_reward = env_sampler.get_reward_pol_embedding_state(args,
init_state, init_obs, emb_policy, policy_decoder, env_idx=ei)[0]
qf = value_net.get_qf(init_obs.unsqueeze(0).to(device), emb_env)
u, s, v = torch.svd(qf.squeeze())
opt_policy_pos = u[:,0].unsqueeze(0)
opt_policy_neg = -u[:,0].unsqueeze(0)
if 'ant' in args.env_name or 'swimmer' in args.env_name:
episode_reward_pos, num_steps_pos = env_sampler.get_reward_pol_embedding_state_mujoco(
args, init_state, init_obs, opt_policy_pos, policy_decoder, env_idx=ei)
episode_reward_neg, num_steps_neg = env_sampler.get_reward_pol_embedding_state_mujoco(
args, init_state, init_obs, opt_policy_neg, policy_decoder, env_idx=ei)
else:
episode_reward_pos, num_steps_pos = env_sampler.get_reward_pol_embedding_state(
args, init_state, init_obs, opt_policy_pos, policy_decoder, env_idx=ei)
episode_reward_neg, num_steps_neg = env_sampler.get_reward_pol_embedding_state(
args, init_state, init_obs, opt_policy_neg, policy_decoder, env_idx=ei)
if episode_reward_pos >= episode_reward_neg:
episode_reward = episode_reward_pos
opt_policy = opt_policy_pos
else:
episode_reward = episode_reward_neg
opt_policy = opt_policy_neg
unnorm_episode_reward = episode_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_init_episode_reward = init_episode_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_decoded_reward = decoded_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_train_rewards[ei].append(unnorm_episode_reward)
train_rewards[ei].append(episode_reward)
if i % args.log_interval == 0:
if 'ant' in args.env_name or 'swimmer' in args.env_name:
print(f"\nTrain Environemnt: {ei} -- top singular value: {s[0].item(): .3f} --- reward after update: {unnorm_episode_reward: .3f}")
print(f"Initial Policy: {pi} --- init true reward: {unnorm_init_episode_reward: .3f} --- decoded: {unnorm_decoded_reward: .3f} --- predicted: {pred_value: .3f}")
print(f"Train Environemnt: {ei} -- top singular value: {s[0].item(): .3f} --- norm reward after update: {episode_reward: .3f}")
print(f"Initial Policy: {pi} --- norm init true reward: {init_episode_reward: .3f} --- norm decoded: {decoded_reward: .3f} --- predicted: {pred_value: .3f}")
all_mean_rewards[ei].append(np.mean(train_rewards[ei]))
all_mean_unnorm_rewards[ei].append(np.mean(unnorm_train_rewards[ei]))
for ei in train_envs:
if 'ant' in args.env_name or 'swimmer' in args.env_name:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_unnorm_rewards[ei]), np.std(all_mean_unnorm_rewards[ei])))
else:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_rewards[ei]), np.std(all_mean_rewards[ei])))
# Eval on Eval Envs
eval_rewards = {}
unnorm_eval_rewards = {}
for ei in range(len(all_envs)):
eval_rewards[ei] = []
unnorm_eval_rewards[ei] = []
for ei in eval_envs:
for i in range(args.num_eval_eps):
args.seed = i
np.random.seed(seed=i)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
for pi in train_policies:
init_obs = torch.FloatTensor(env_sampler.env.reset(env_id=ei))
if 'ant' in args.env_name or 'swimmer' in args.env_name:
init_state = env_sampler.env.sim.get_state()
res = env_sampler.zeroshot_sample_src_from_pol_state_mujoco(args, init_obs, sizes, policy_idx=pi, env_idx=ei)
else:
init_state = env_sampler.env.state
res = env_sampler.zeroshot_sample_src_from_pol_state(args, init_obs, sizes, policy_idx=pi, env_idx=ei)
source_env = res['source_env']
mask_env = res['mask_env']
source_policy = res['source_policy']
init_episode_reward = res['episode_reward']
mask_policy = res['mask_policy']
if source_policy.shape[1] == 1:
source_policy = source_policy.repeat(1, 2, 1)
mask_policy = mask_policy.repeat(1, 1, 2)
emb_policy = policy_encoder(source_policy.detach().to(device),
mask_policy.detach().to(device)).detach()
if source_env.shape[1] == 1:
source_env = source_env.repeat(1, 2, 1)
mask_env = mask_env.repeat(1, 1, 2)
emb_env = env_encoder(source_env.detach().to(device),
mask_env.detach().to(device)).detach()
emb_policy = F.normalize(emb_policy, p=2, dim=1).detach()
emb_env = F.normalize(emb_env, p=2, dim=1).detach()
pred_value = value_net(init_obs.unsqueeze(0).to(device),
emb_env.to(device), emb_policy.to(device)).item()
if 'ant' in args.env_name or 'swimmer' in args.env_name:
decoded_reward = env_sampler.get_reward_pol_embedding_state_mujoco(args,
init_state, init_obs, emb_policy, policy_decoder, env_idx=ei)[0]
else:
decoded_reward = env_sampler.get_reward_pol_embedding_state(args,
init_state, init_obs, emb_policy, policy_decoder, env_idx=ei)[0]
qf = value_net.get_qf(init_obs.unsqueeze(0).to(device), emb_env)
u, s, v = torch.svd(qf.squeeze())
opt_policy_pos = u[:,0].unsqueeze(0)
opt_policy_neg = -u[:,0].unsqueeze(0)
if 'ant' in args.env_name or 'swimmer' in args.env_name:
episode_reward_pos, num_steps_pos = env_sampler.get_reward_pol_embedding_state_mujoco(
args, init_state, init_obs, opt_policy_pos, policy_decoder, env_idx=ei)
episode_reward_neg, num_steps_neg = env_sampler.get_reward_pol_embedding_state_mujoco(
args, init_state, init_obs, opt_policy_neg, policy_decoder, env_idx=ei)
else:
episode_reward_pos, num_steps_pos = env_sampler.get_reward_pol_embedding_state(
args, init_state, init_obs, opt_policy_pos, policy_decoder, env_idx=ei)
episode_reward_neg, num_steps_neg = env_sampler.get_reward_pol_embedding_state(
args, init_state, init_obs, opt_policy_neg, policy_decoder, env_idx=ei)
if episode_reward_pos >= episode_reward_neg:
episode_reward = episode_reward_pos
opt_policy = opt_policy_pos
else:
episode_reward = episode_reward_neg
opt_policy = opt_policy_neg
unnorm_episode_reward = episode_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_init_episode_reward = init_episode_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_decoded_reward = decoded_reward * (args.max_reward - args.min_reward) + args.min_reward
unnorm_eval_rewards[ei].append(unnorm_episode_reward)
eval_rewards[ei].append(episode_reward)
if i % args.log_interval == 0:
if 'ant' in args.env_name or 'swimmer' in args.env_name:
print(f"\nEval Environemnt: {ei} -- top singular value: {s[0].item(): .3f} --- reward after update: {unnorm_episode_reward: .3f}")
print(f"Initial Policy: {pi} --- init true reward: {unnorm_init_episode_reward: .3f} --- decoded: {unnorm_decoded_reward: .3f} --- predicted: {pred_value: .3f}")
print(f"Eval Environemnt: {ei} -- top singular value: {s[0].item(): .3f} --- norm reward after update: {episode_reward: .3f}")
print(f"Initial Policy: {pi} --- norm init true reward: {init_episode_reward: .3f} --- norm decoded: {decoded_reward: .3f} --- predicted: {pred_value: .3f}")
all_mean_rewards[ei].append(np.mean(eval_rewards[ei]))
all_mean_unnorm_rewards[ei].append(np.mean(unnorm_eval_rewards[ei]))
for ei in train_envs:
if 'ant' in args.env_name or 'swimmer' in args.env_name:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_unnorm_rewards[ei]), np.std(all_mean_unnorm_rewards[ei])))
else:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_rewards[ei]), np.std(all_mean_rewards[ei])))
for ei in eval_envs:
if 'ant' in args.env_name or 'swimmer' in args.env_name:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_unnorm_rewards[ei]), np.std(all_mean_unnorm_rewards[ei])))
else:
print("Train Env {} has reward with mean {:.3f} and std {:.3f}"\
.format(ei, np.mean(all_mean_rewards[ei]), np.std(all_mean_rewards[ei])))
env.close()
if __name__ == '__main__':
eval_pdvf()