def __init__(self, load_path, algorithm, policy_name, observation_space, action_space):
self._checkpoint_path = load_path
self._policy_name = policy_name
self._observation_space = observation_space
self._action_space = action_space
self._prep = ModelCatalog.get_preprocessor_for_space(self._observation_space)
flat_obs_space = self._prep.observation_space
ray.init(ignore_reinit_error=True, local_mode=True)
from utils.ppo_policy import PPOTorchPolicy as LoadPolicy
from utils.rnn_model import RNNDVEModel
ModelCatalog.register_custom_model("my_rnn", RNNDVEModel)
config = ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy()
config['num_workers'] = 0
config["model"]["custom_model"] = "my_rnn"
self.policy = LoadPolicy(flat_obs_space, self._action_space, config)
objs = pickle.load(open(self._checkpoint_path, "rb"))
objs = pickle.loads(objs["worker"])
state = objs["state"]
filters = objs["filters"]
self.filters = filters[self._policy_name]
weights = state[self._policy_name]
weights.pop("_optimizer_variables")
self.policy.set_weights(weights)
self.model = self.policy.model
self.rnn_state = self.model.get_initial_state()
self.rnn_state = [torch.reshape(self.rnn_state[0], shape=(1, -1))]
def __init__(self, load_path, algorithm, policy_name, observation_space, action_space):
self._checkpoint_path = load_path
self._policy_name = policy_name
self._observation_space = observation_space
self._action_space = action_space
self._prep = ModelCatalog.get_preprocessor_for_space(self._observation_space)
flat_obs_space = self._prep.observation_space
ray.init(ignore_reinit_error=True, local_mode=True)
from utils.ppo_policy import PPOTorchPolicy as LoadPolicy
config = ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy()
config['num_workers'] = 0
config["model"]["use_lstm"] = True
config['model']['free_log_std'] = False
self.policy = LoadPolicy(flat_obs_space, self._action_space, config)
objs = pickle.load(open(self._checkpoint_path, "rb"))
objs = pickle.loads(objs["worker"])
state = objs["state"]
filters = objs["filters"]
self.filters = filters[self._policy_name]
weights = state[self._policy_name]
weights.pop("_optimizer_variables")
self.policy.set_weights(weights)
self.model = self.policy.model
self.rnn_state = self.model.get_initial_state()
self.rnn_state = [self.rnn_state[0].unsqueeze(0),self.rnn_state[1].unsqueeze(0)]
def __init__(self, load_path, algorithm, policy_name, observation_space, action_space):
self._checkpoint_path = load_path
self._policy_name = policy_name
self._observation_space = observation_space
self._action_space = action_space
self._prep = ModelCatalog.get_preprocessor_for_space(self._observation_space)
flat_obs_space = self._prep.observation_space
ray.init(ignore_reinit_error=True, local_mode=True)
from utils.ppo_policy import PPOTorchPolicy as LoadPolicy
from utils.fc_model import FCMultiLayerNetwork
ModelCatalog.register_custom_model("my_fc", FCMultiLayerNetwork)
config = ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy()
config["vf_share_layers"] = True
config['num_workers'] = 0
config["model"]["custom_model"] = "my_fc"
config['model']['free_log_std'] = False
self.policy = LoadPolicy(flat_obs_space, self._action_space, config)
objs = pickle.load(open(self._checkpoint_path, "rb"))
objs = pickle.loads(objs["worker"])
state = objs["state"]
filters = objs["filters"]
self.filters = filters[self._policy_name]
weights = state[self._policy_name]
weights.pop("_optimizer_variables")
self.policy.set_weights(weights)
self.model = self.policy.model
class RLlibTorchGRUMultiPolicy(AgentPolicy):
def __init__(self, load_path, algorithm, policy_name, observation_space, action_space):
self._checkpoint_path = load_path
self._policy_name = policy_name
self._observation_space = observation_space
self._action_space = action_space
self._prep = ModelCatalog.get_preprocessor_for_space(self._observation_space)
flat_obs_space = self._prep.observation_space
ray.init(ignore_reinit_error=True, local_mode=True)
from utils.ppo_policy import PPOTorchPolicy as LoadPolicy
from utils.rnn_model import RNNMultiModel
ModelCatalog.register_custom_model("my_rnn", RNNMultiModel)
config = ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy()
config["vf_share_layers"] = True
config['num_workers'] = 0
config["model"]["custom_model"] = "my_rnn"
config['model']['free_log_std'] = False
self.policy = LoadPolicy(flat_obs_space, self._action_space, config)
objs = pickle.load(open(self._checkpoint_path, "rb"))
objs = pickle.loads(objs["worker"])
state = objs["state"]
filters = objs["filters"]
self.filters = filters[self._policy_name]
weights = state[self._policy_name]
weights.pop("_optimizer_variables")
self.policy.set_weights(weights)
self.model = self.policy.model
self.rnn_state = self.model.get_initial_state()
self.rnn_state = [torch.reshape(self.rnn_state[0], shape=(1, -1))]
def act(self, obs):
# single infer
obs = self._prep.transform(obs)
obs = self.filters(obs, update=False)
action, self.rnn_state, _ = self.policy.compute_actions([obs], self.rnn_state, explore=False)
action = action[0]
return action
class RLlibTorchFCDyskipPolicy(AgentPolicy):
def __init__(self, load_path, algorithm, policy_name, observation_space, action_space):
self._checkpoint_path = load_path
self._policy_name = policy_name
self._observation_space = observation_space
self._action_space = action_space
self._prep = ModelCatalog.get_preprocessor_for_space(self._observation_space)
flat_obs_space = self._prep.observation_space
ray.init(ignore_reinit_error=True, local_mode=True)
from utils.ppo_policy import PPOTorchPolicy as LoadPolicy
from utils.fc_model import FullyConnectedNetwork
ModelCatalog.register_custom_model("my_fc", FullyConnectedNetwork)
ModelCatalog.register_custom_action_dist("my_dist", TorchDyDistribution)
config = ray.rllib.agents.ppo.ppo.DEFAULT_CONFIG.copy()
config['num_workers'] = 0
config["model"]["custom_model"] = "my_fc"
config['model']['free_log_std'] = False
config["model"]["custom_action_dist"] = "my_dist"
self.policy = LoadPolicy(flat_obs_space, self._action_space, config)
objs = pickle.load(open(self._checkpoint_path, "rb"))
objs = pickle.loads(objs["worker"])
state = objs["state"]
filters = objs["filters"]
self.filters = filters[self._policy_name]
weights = state[self._policy_name]
weights.pop("_optimizer_variables")
self.policy.set_weights(weights)
self.model = self.policy.model
def act(self, obs):
# single infer
obs = self._prep.transform(obs)
obs = self.filters(obs, update=False)
action, _, _ = self.policy.compute_actions([obs], explore=False)
action = action[0]
return action