def discrete_atari_env():
env = AtariEnvironment(name="MsPacman-v0",
clone_seeds=True,
autoreset=True)
env.reset()
env = DiscreteEnv(env)
return env
def classic_control_env():
env = ClassicControl()
env.reset()
env = DiscreteEnv(env)
params = {"actions": {"dtype": np.int64}, "dt": {"dtype": np.float32}}
states = States(state_dict=params, batch_size=N_WALKERS)
states.update(actions=np.ones(N_WALKERS), dt=np.ones(N_WALKERS))
return env, states
def create_cartpole_swarm():
swarm = Swarm(
model=lambda x: DiscreteUniform(env=x),
walkers=Walkers,
env=lambda: DiscreteEnv(ClassicControl("CartPole-v0")),
reward_limit=121,
n_walkers=150,
max_epochs=300,
reward_scale=2,
)
return swarm
def create_cartpole_swarm():
swarm = Swarm(
model=lambda x: DiscreteUniform(env=x),
walkers=Walkers,
env=lambda: DiscreteEnv(ClassicControl()),
n_walkers=20,
max_iters=200,
prune_tree=True,
reward_scale=2,
)
return swarm
def create_atari_swarm():
env = AtariEnvironment(name="MsPacman-ram-v0", )
dt = GaussianDt(min_dt=10, max_dt=100, loc_dt=5, scale_dt=2)
swarm = Swarm(
model=lambda x: DiscreteUniform(env=x, critic=dt),
env=lambda: DiscreteEnv(env),
n_walkers=6,
max_epochs=10,
reward_scale=2,
reward_limit=1,
)
return swarm
def create_atari_swarm():
env = AtariEnvironment(name="MsPacman-ram-v0",
clone_seeds=True,
autoreset=True)
dt = GaussianDt(min_dt=3, max_dt=100, loc_dt=5, scale_dt=2)
swarm = Swarm(
model=lambda x: DiscreteUniform(env=x, critic=dt),
walkers=Walkers,
env=lambda: DiscreteEnv(env),
n_walkers=67,
max_epochs=500,
reward_scale=2,
reward_limit=751,
)
return swarm
def atari_env():
env = AtariEnvironment(name="MsPacman-v0",
clone_seeds=True,
autoreset=True)
env.reset()
env = DiscreteEnv(env)
params = {
"actions": {
"dtype": np.int64
},
"critic": {
"dtype": np.float32
}
}
states = States(state_dict=params, batch_size=N_WALKERS)
states.update(actions=np.ones(N_WALKERS), critic=np.ones(N_WALKERS))
return env, states
def create_atari_swarm():
env = ParallelEnvironment(
env_class=AtariEnvironment,
name="MsPacman-ram-v0",
clone_seeds=True,
autoreset=True,
blocking=False,
)
dt = GaussianDt(min_dt=3, max_dt=100, loc_dt=5, scale_dt=2)
swarm = Swarm(
model=lambda x: DiscreteUniform(env=x, critic=dt),
walkers=Walkers,
env=lambda: DiscreteEnv(env),
n_walkers=67,
max_iters=20,
prune_tree=True,
reward_scale=2,
)
return swarm
def env(self):
from fragile.core.env import DiscreteEnv
return DiscreteEnv(DummyEnv())
def classic_control_env():
env = ClassicControl()
env.reset()
env = DiscreteEnv(env)
return env