def __init__(self, params):
self.params = params
train_args = {
'num_agent_train_steps_per_iter':
params['num_agent_train_steps_per_iter'],
'num_critic_updates_per_agent_update':
params['num_critic_updates_per_agent_update'],
'train_batch_size':
params['batch_size'],
'double_q':
params['double_q'],
'device':
params['device'],
}
env_args = get_env_kwargs(params['env_name'])
self.agent_params = {**train_args, **env_args, **params}
self.params['agent_class'] = DQNAgent
self.params['agent_params'] = self.agent_params
self.params['train_batch_size'] = params['batch_size']
self.params['env_wrappers'] = self.agent_params['env_wrappers']
self.rl_trainer = RL_Trainer(self.params)
def __init__(self, params):
self.params = params
train_args = {
"num_agent_train_steps_per_iter": params["num_agent_train_steps_per_iter"],
"num_critic_updates_per_agent_update": params[
"num_critic_updates_per_agent_update"
],
"train_batch_size": params["batch_size"],
"double_q": params["double_q"],
}
env_args = get_env_kwargs(params["env_name"])
self.agent_params = {**train_args, **env_args, **params}
self.params["agent_class"] = ExplorationOrExploitationAgent
self.params["agent_params"] = self.agent_params
self.params["train_batch_size"] = params["batch_size"]
self.params["env_wrappers"] = self.agent_params["env_wrappers"]
self.rl_trainer = RL_Trainer(self.params)
