def __init__(self):
td3_algorithm_params = TD3AlgorithmParameters()
super().__init__(algorithm=td3_algorithm_params,
exploration=TD3AgentExplorationParameters(),
memory=EpisodicExperienceReplayParameters(),
networks=OrderedDict([("actor", TD3ActorNetworkParameters()),
("critic",
TD3CriticNetworkParameters(td3_algorithm_params.num_q_networks))]))
def __init__(self):
super().__init__(algorithm=DDPGAlgorithmParameters(),
exploration=OUProcessParameters(),
memory=EpisodicExperienceReplayParameters(),
networks=OrderedDict([
("actor", DDPGActorNetworkParameters()),
("critic", DDPGCriticNetworkParameters())
]))
def __init__(self):
super().__init__(algorithm=ClippedPPOAlgorithmParameters(),
exploration={
DiscreteActionSpace: CategoricalParameters(),
BoxActionSpace: AdditiveNoiseParameters()
},
memory=EpisodicExperienceReplayParameters(),
networks={"main": ClippedPPONetworkParameters()})
def __init__(self):
super().__init__(algorithm=DDPGAlgorithmParameters(),
exploration=OUProcessParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={
"actor": DDPGActorNetworkParameters(),
"critic": DDPGCriticNetworkParameters()
})
def __init__(self):
super().__init__(algorithm=PPOAlgorithmParameters(),
exploration=AdditiveNoiseParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={
"critic": PPOCriticNetworkParameters(),
"actor": PPOActorNetworkParameters()
})
def __init__(self):
super().__init__(algorithm=PPOAlgorithmParameters(),
exploration={
DiscreteActionSpace: CategoricalParameters(),
BoxActionSpace: AdditiveNoiseParameters()
},
memory=EpisodicExperienceReplayParameters(),
networks={
"critic": PPOCriticNetworkParameters(),
"actor": PPOActorNetworkParameters()
})
def __init__(self, agent_index, use_batchnorm=False):
# def __init__(self, use_batchnorm=False, name, model, obs_shape_n, act_space_n, agent_index, local_q_func=False):
# self.agent_index = agent_index
super().__init__(
algorithm=MADDPGAlgorithmParameters(),
exploration=OUProcessParameters(),
memory=EpisodicExperienceReplayParameters(),
networks=OrderedDict([
("actor" + str(agent_index),
MADDPGActorNetworkParameters(use_batchnorm=use_batchnorm)),
("critic" + str(agent_index),
MADDPGCriticNetworkParameters(use_batchnorm=use_batchnorm))
]))
def __init__(self):
super().__init__()
self.algorithm = PALAlgorithmParameters()
self.memory = EpisodicExperienceReplayParameters()
def __init__(self):
super().__init__(
algorithm=ACERAlgorithmParameters(),
exploration={DiscreteActionSpace: CategoricalParameters()},
memory=EpisodicExperienceReplayParameters(),
networks={"main": ACERNetworkParameters()})
def __init__(self):
super().__init__(algorithm=NAFAlgorithmParameters(),
exploration=OUProcessParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={"main": NAFNetworkParameters()})
def __init__(self):
super().__init__(algorithm=HumanAlgorithmParameters(),
exploration=EGreedyParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={"main": BCNetworkParameters()})
def __init__(self):
super().__init__()
self.algorithm = MixedMonteCarloAlgorithmParameters()
self.memory = EpisodicExperienceReplayParameters()
def __init__(self):
super().__init__(algorithm=ClippedPPOAlgorithmParameters(),
exploration=AdditiveNoiseParameters(),
memory=EpisodicExperienceReplayParameters(),
networks={"main": ClippedPPONetworkParameters()})
