def _initialize(self):
"""Initialize agent components."""
# Define env specific model params
self.experiment_info.env.state_dim = self.env.observation_space.shape[
0]
self.experiment_info.env.action_dim = self.env.action_space.shape[0]
self.experiment_info.env.action_range = [
self.env.action_space.low.tolist(),
self.env.action_space.high.tolist(),
]
# Build learner
self.learner = build_learner(self.experiment_info, self.hyper_params,
self.model_cfg)
# Build replay buffer, wrap with PER buffer if using it
self.replay_buffer = ReplayBuffer(self.hyper_params)
if self.hyper_params.use_per:
self.replay_buffer = PrioritizedReplayBuffer(
self.replay_buffer, self.hyper_params)
# Build action selector
self.action_selector = build_action_selector(self.experiment_info)
self.action_selector = OUNoise(self.action_selector,
self.env.action_space)
# Build logger
if self.experiment_info.log_wandb:
experiment_cfg = OmegaConf.create(
dict(
experiment_info=self.experiment_info,
hyper_params=self.hyper_params,
model=self.learner.model_cfg,
))
self.logger = Logger(experiment_cfg)
def _initialize(self):
"""Initialize agent components"""
# set env specific model params
self.model_cfg.params.model_cfg.state_dim = self.env.observation_space.shape
self.model_cfg.params.model_cfg.action_dim = self.env.action_space.n
# Build learner
self.learner = build_learner(
self.experiment_info, self.hyper_params, self.model_cfg
)
# Build replay buffer, wrap with PER buffer if using it
self.replay_buffer = ReplayBuffer(self.hyper_params)
if self.hyper_params.use_per:
self.replay_buffer = PrioritizedReplayBuffer(
self.replay_buffer, self.hyper_params
)
# Build action selector, wrap with e-greedy exploration
self.action_selector = build_action_selector(self.experiment_info)
self.action_selector = EpsGreedy(
self.action_selector, self.env.action_space, self.hyper_params
)
if self.experiment_info.log_wandb:
experiment_cfg = OmegaConf.create(
dict(
experiment_info=self.experiment_info,
hyper_params=self.hyper_params,
model=self.learner.model_cfg,
)
)
self.logger = Logger(experiment_cfg)
def _initialize(self):
"""Set env specific configs and build learner."""
self.experiment_info.env.state_dim = self.env.observation_space.shape[
0]
if self.experiment_info.env.is_discrete:
self.experiment_info.env.action_dim = self.env.action_space.n
else:
self.experiment_info.env.action_dim = self.env.action_space.shape[
0]
self.experiment_info.env.action_range = [
self.env.action_space.low.tolist(),
self.env.action_space.high.tolist(),
]
self.learner = build_learner(self.experiment_info, self.hyper_params,
self.model_cfg)
self.action_selector = build_action_selector(self.experiment_info,
self.use_cuda)
# Build logger
if self.experiment_info.log_wandb:
experiment_cfg = OmegaConf.create(
dict(
experiment_info=self.experiment_info,
hyper_params=self.hyper_params,
model=self.learner.model_cfg,
))
self.logger = Logger(experiment_cfg)
def main(cfg: DictConfig):
# print all configs
print(cfg.pretty())
# build env
print("===INITIALIZING ENV===")
env = build_env(cfg.experiment_info)
print(env.reset())
print("=================")
# build model
print("===INITIALIZING MODEL===")
cfg.model.params.model_cfg.state_dim = env.observation_space.shape
cfg.model.params.model_cfg.action_dim = env.action_space.n
cfg.model.params.model_cfg.fc.output.params.output_size = env.action_space.n
model = build_model(cfg.model)
test_input = torch.FloatTensor(env.reset()).unsqueeze(0)
print(model)
print(model.forward(test_input))
print("===================")
# build action_selector
print("===INITIALIZING ACTION SELECTOR===")
action_selector = build_action_selector(cfg.experiment_info)
print(action_selector)
print("==============================")
# build loss
print("===INITIALIZING LOSS===")
loss = build_loss(cfg.experiment_info)
print(loss)
print("==================")
# build learner
print("===INITIALIZING LEARNER===")
learner = build_learner(**cfg)
print(learner)
print("=====================")
# build agent
print("===INITIALIZING AGENT===")
agent = build_agent(**cfg)
print(agent)
print("=====================")