def init_agent(self):
observation_space, action_space = utils.get_gym_spaces(
self.runner.make_env)
agent = BaseDDPGAgent(observation_space,
action_space,
actor_lr=self.hparams.actor_lr,
critic_lr=self.hparams.critic_lr,
gamma=self.hparams.gamma,
tau=self.hparams.tau)
return agent
def init_agent(self):
observation_space, action_space = utils.get_gym_spaces(
self.runner.make_env)
agent = BaseA2CAgent(observation_space,
action_space,
lr=self.hparams.actor_lr,
gamma=self.hparams.gamma,
lmbda=self.hparams.lmbda,
alpha=self.hparams.alpha,
beta=self.hparams.beta)
return agent
def init_agent(self):
observation_space, action_space = utils.get_gym_spaces(self.runner.make_env)
agent = BaseDQNAgent(
observation_space,
action_space,
double_dqn=self.hparams.double_dqn,
lr=self.hparams.actor_lr,
gamma=self.hparams.gamma,
num_eps_steps=self.hparams.num_eps_steps,
target_update_interval=self.hparams.target_update_interval)
return agent
def test_gym_runner(env_id: str):
runner = GymRunner(env_id)
observation_space, action_space = get_gym_spaces(runner.make_env)
agent = GymRandomAgent(observation_space, action_space)
trajectory_list = runner.rollout(agent)
runner.close()
assert len(trajectory_list) == runner.n_envs
assert trajectory_list[0].obs.ndim == 2
assert trajectory_list[0].action.ndim == 2
assert trajectory_list[0].reward.ndim == 2
assert trajectory_list[0].next_obs.ndim == 2
assert trajectory_list[0].done.ndim == 2
def test_gym_runner(env_id: str):
runner = GymRunner(env_id)
agent = GymRandomAgent(*get_gym_spaces(runner.make_env))
runner.rollout(agent)
runner.close()
def init_agent(self):
observation_space, action_space = utils.get_gym_spaces(
self.runner.make_env)
return GymRandomAgent(observation_space, action_space)
