def create_symbolic_action_distributions(action_space, base_output_size):
if action_space == "full":
bernoulli_dist = distributions.Bernoulli(base_output_size, 2)
item_dist = distributions.Categorical(base_output_size, 6)
quantity_dist = distributions.Categorical(base_output_size, 5)
move_dist = distributions.Categorical(base_output_size, 4)
# clear_dist = distributions.Categorical(base_output_size, 4)
dist = distributions.DistributionGeneratorTuple(
(bernoulli_dist, item_dist, quantity_dist, move_dist))
elif action_space == "move-only":
bernoulli_dist = distributions.Bernoulli(base_output_size, 1)
move_dist = distributions.Categorical(base_output_size, 4)
dist = distributions.DistributionGeneratorTuple(
(bernoulli_dist, move_dist))
elif action_space == "move-continuous":
bernoulli_dist = distributions.Bernoulli(base_output_size, 1)
move_dist = distributions.DiagGaussian(base_output_size, 2)
dist = distributions.DistributionGeneratorTuple(
(bernoulli_dist, move_dist))
elif action_space == "move-uniform":
bernoulli_dist = distributions.Bernoulli(base_output_size, 1)
move_x = distributions.Categorical(base_output_size, 9)
move_y = distributions.Categorical(base_output_size, 9)
dist = distributions.DistributionGeneratorTuple(
(bernoulli_dist, move_x, move_y))
elif action_space == "rooms":
action_dist = distributions.Categorical(base_output_size, 3)
move_x = distributions.Categorical(base_output_size, 5)
move_y = distributions.Categorical(base_output_size, 5)
item_dist = distributions.Categorical(base_output_size, 6)
quantity_dist = distributions.Categorical(base_output_size, 5)
dist = distributions.DistributionGeneratorTuple(
(action_dist, move_x, move_y, item_dist, quantity_dist))
return dist
def experiment(variant):
common.initialise(variant)
expl_envs, eval_envs = common.create_environments(variant)
(
obs_shape,
obs_space,
action_space,
n,
mlp,
channels,
fc_input,
) = common.get_spaces(expl_envs)
obs_dim = obs_shape[1]
qf = CNN(
input_width=obs_dim,
input_height=obs_dim,
input_channels=channels,
output_size=8,
kernel_sizes=[8, 4],
n_channels=[16, 32],
strides=[4, 2],
paddings=[0, 0],
hidden_sizes=[256],
)
# CHANGE TO ORDINAL ACTION SPACE
action_space = gym.spaces.Box(-np.inf, np.inf, (8, ))
expl_envs.action_space = action_space
eval_envs.action_space = action_space
base = common.create_networks(variant, n, mlp, channels, fc_input)
bernoulli_dist = distributions.Bernoulli(base.output_size, 4)
passenger_dist = distributions.Categorical(base.output_size, 5)
delivered_dist = distributions.Categorical(base.output_size, 5)
continuous_dist = distributions.DiagGaussian(base.output_size, 2)
dist = distributions.DistributionGeneratorTuple(
(bernoulli_dist, continuous_dist, passenger_dist, delivered_dist))
eval_policy = LearnPlanPolicy(
ScriptedPolicy(qf, variant["always_return"]),
num_processes=variant["num_processes"],
vectorised=True,
json_to_screen=expl_envs.observation_space.converter,
)
expl_policy = LearnPlanPolicy(
ScriptedPolicy(qf, variant["always_return"]),
num_processes=variant["num_processes"],
vectorised=True,
json_to_screen=expl_envs.observation_space.converter,
)
eval_path_collector = HierarchicalStepCollector(
eval_envs,
eval_policy,
ptu.device,
max_num_epoch_paths_saved=variant["algorithm_kwargs"]
["num_eval_steps_per_epoch"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=1,
no_plan_penalty=variant.get("no_plan_penalty", False),
)
expl_path_collector = HierarchicalStepCollector(
expl_envs,
expl_policy,
ptu.device,
max_num_epoch_paths_saved=variant["num_steps"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=variant["trainer_kwargs"]["gamma"],
no_plan_penalty=variant.get("no_plan_penalty", False),
)
# added: created rollout(5,1,(4,84,84),Discrete(6),1), reset env and added obs to rollout[step]
trainer = PPOTrainer(actor_critic=expl_policy.learner,
**variant["trainer_kwargs"])
# missing: by this point, rollout back in sync.
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_envs)
# added: replay buffer is new
algorithm = TorchIkostrikovRLAlgorithm(
trainer=trainer,
exploration_env=expl_envs,
evaluation_env=eval_envs,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"],
# batch_size,
# max_path_length,
# num_epochs,
# num_eval_steps_per_epoch,
# num_expl_steps_per_train_loop,
# num_trains_per_train_loop,
# num_train_loops_per_epoch=1,
# min_num_steps_before_training=0,
)
algorithm.to(ptu.device)
# missing: device back in sync
algorithm.evaluate()
def experiment(variant):
common.initialise(variant)
expl_envs, eval_envs = common.create_environments(variant)
(
obs_shape,
obs_space,
action_space,
n,
mlp,
channels,
fc_input,
) = common.get_spaces(expl_envs)
# # CHANGE TO ORDINAL ACTION SPACE
# action_space = gym.spaces.Box(-np.inf, np.inf, (8,))
# expl_envs.action_space = action_space
# eval_envs.action_space = action_space
ANCILLARY_GOAL_SIZE = variant["ancillary_goal_size"]
SYMBOLIC_ACTION_SIZE = 12
base = common.create_networks(variant, n, mlp, channels, fc_input)
control_base = common.create_networks(
variant, n, mlp, channels,
fc_input + SYMBOLIC_ACTION_SIZE) # for uvfa goal representation
dist = common.create_symbolic_action_distributions(variant["action_space"],
base.output_size)
control_dist = distributions.Categorical(base.output_size, action_space.n)
eval_learner = WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=base,
deterministic=True,
dist=dist,
num_processes=variant["num_processes"],
obs_space=obs_space,
)
planner = ENHSPPlanner()
# multihead
# eval_controller = CraftController(
# MultiPolicy(
# obs_shape,
# action_space,
# ptu.device,
# 18,
# base=base,
# deterministic=True,
# num_processes=variant["num_processes"],
# obs_space=obs_space,
# )
# )
# expl_controller = CraftController(
# MultiPolicy(
# obs_shape,
# action_space,
# ptu.device,
# 18,
# base=base,
# deterministic=False,
# num_processes=variant["num_processes"],
# obs_space=obs_space,
# )
# )
# uvfa
eval_controller = CraftController(
WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=control_base,
dist=control_dist,
deterministic=True,
num_processes=variant["num_processes"],
obs_space=obs_space,
symbolic_action_size=SYMBOLIC_ACTION_SIZE,
),
n=n,
)
expl_controller = CraftController(
WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=control_base,
dist=control_dist,
deterministic=False,
num_processes=variant["num_processes"],
obs_space=obs_space,
symbolic_action_size=SYMBOLIC_ACTION_SIZE,
),
n=n,
)
function_env = gym.make(variant["env_name"])
eval_policy = LearnPlanPolicy(
eval_learner,
planner,
eval_controller,
num_processes=variant["num_processes"],
vectorised=True,
env=function_env,
)
expl_learner = WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=base,
deterministic=False,
dist=dist,
num_processes=variant["num_processes"],
obs_space=obs_space,
)
expl_policy = LearnPlanPolicy(
expl_learner,
planner,
expl_controller,
num_processes=variant["num_processes"],
vectorised=True,
env=function_env,
)
eval_path_collector = ThreeTierStepCollector(
eval_envs,
eval_policy,
ptu.device,
ANCILLARY_GOAL_SIZE,
SYMBOLIC_ACTION_SIZE,
max_num_epoch_paths_saved=variant["algorithm_kwargs"]
["num_eval_steps_per_epoch"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=1,
no_plan_penalty=True,
meta_num_epoch_paths=variant["meta_num_steps"],
)
expl_path_collector = ThreeTierStepCollector(
expl_envs,
expl_policy,
ptu.device,
ANCILLARY_GOAL_SIZE,
SYMBOLIC_ACTION_SIZE,
max_num_epoch_paths_saved=variant["num_steps"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=variant["trainer_kwargs"]["gamma"],
no_plan_penalty=variant.get("no_plan_penalty", False),
meta_num_epoch_paths=variant["meta_num_steps"],
)
# added: created rollout(5,1,(4,84,84),Discrete(6),1), reset env and added obs to rollout[step]
learn_trainer = PPOTrainer(actor_critic=expl_policy.learner,
**variant["trainer_kwargs"])
control_trainer = PPOTrainer(actor_critic=expl_policy.controller.policy,
**variant["trainer_kwargs"])
trainer = MultiTrainer([control_trainer, learn_trainer])
# missing: by this point, rollout back in sync.
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_envs)
# added: replay buffer is new
algorithm = TorchIkostrikovRLAlgorithm(
trainer=trainer,
exploration_env=expl_envs,
evaluation_env=eval_envs,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"],
# batch_size,
# max_path_length,
# num_epochs,
# num_eval_steps_per_epoch,
# num_expl_steps_per_train_loop,
# num_trains_per_train_loop,
# num_train_loops_per_epoch=1,
# min_num_steps_before_training=0,
)
algorithm.to(ptu.device)
algorithm.train()
def experiment(variant):
common.initialise(variant)
expl_envs, eval_envs = common.create_environments(variant)
(
obs_shape,
obs_space,
action_space,
n,
mlp,
channels,
fc_input,
) = common.get_spaces(expl_envs)
# # CHANGE TO ORDINAL ACTION SPACE
# action_space = gym.spaces.Box(-np.inf, np.inf, (8,))
# expl_envs.action_space = action_space
# eval_envs.action_space = action_space
ANCILLARY_GOAL_SIZE = variant[
"ancillary_goal_size"] # This is the length of the action space for the learner
SYMBOLIC_ACTION_SIZE = 12
GRID_SIZE = 31
base = common.create_networks(variant, n, mlp, channels, fc_input)
control_base = common.create_networks(
variant, n, mlp, channels,
fc_input + SYMBOLIC_ACTION_SIZE) # for uvfa goal representation
dist = common.create_symbolic_action_distributions(variant["action_space"],
base.output_size)
control_dist = distributions.Categorical(base.output_size, action_space.n)
eval_learner = WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=base,
deterministic=True,
dist=dist,
num_processes=variant["num_processes"],
obs_space=obs_space,
)
planner = ENHSPPlanner()
# collect
filepath = "/home/achester/anaconda3/envs/goal-gen/.guild/runs/e77c75eed02e4b38a0a308789fbfcbd8/data/params.pkl" # collect
with (open(filepath, "rb")) as openfile:
while True:
try:
policies = pickle.load(openfile)
except EOFError:
break
loaded_collect_policy = policies["exploration/policy"]
loaded_collect_policy.rnn_hxs = loaded_collect_policy.rnn_hxs[0].unsqueeze(
0)
eval_collect = CraftController(loaded_collect_policy, n=GRID_SIZE)
expl_collect = CraftController(loaded_collect_policy, n=GRID_SIZE)
# other
filepath = "/home/achester/anaconda3/envs/goal-gen/.guild/runs/cf5c31afe0724acd8f6398d77a80443e/data/params.pkl" # other
# filepath = "/home/achester/Documents/symbolic-goal-generation/data/params.pkl"
with (open(filepath, "rb")) as openfile:
while True:
try:
policies = pickle.load(openfile)
except EOFError:
break
loaded_other_policy = policies["exploration/policy"]
loaded_other_policy.rnn_hxs = loaded_other_policy.rnn_hxs[0].unsqueeze(0)
eval_other = CraftController(loaded_other_policy, n=GRID_SIZE)
expl_other = CraftController(loaded_other_policy, n=GRID_SIZE)
eval_controller = PretrainedController([eval_collect, eval_other])
expl_controller = PretrainedController([expl_collect, expl_other])
function_env = gym.make(variant["env_name"])
eval_policy = LearnPlanPolicy(
eval_learner,
planner,
eval_controller,
num_processes=variant["num_processes"],
vectorised=True,
env=function_env,
)
expl_learner = WrappedPolicy(
obs_shape,
action_space,
ptu.device,
base=base,
deterministic=False,
dist=dist,
num_processes=variant["num_processes"],
obs_space=obs_space,
)
expl_policy = LearnPlanPolicy(
expl_learner,
planner,
expl_controller,
num_processes=variant["num_processes"],
vectorised=True,
env=function_env,
)
eval_path_collector = ThreeTierStepCollector(
eval_envs,
eval_policy,
ptu.device,
ANCILLARY_GOAL_SIZE,
SYMBOLIC_ACTION_SIZE,
max_num_epoch_paths_saved=variant["algorithm_kwargs"]
["num_eval_steps_per_epoch"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=1,
no_plan_penalty=True,
meta_num_epoch_paths=variant["meta_num_steps"],
)
expl_path_collector = ThreeTierStepCollector(
expl_envs,
expl_policy,
ptu.device,
ANCILLARY_GOAL_SIZE,
SYMBOLIC_ACTION_SIZE,
max_num_epoch_paths_saved=variant["num_steps"],
num_processes=variant["num_processes"],
render=variant["render"],
gamma=variant["trainer_kwargs"]["gamma"],
no_plan_penalty=variant.get("no_plan_penalty", False),
meta_num_epoch_paths=variant["meta_num_steps"],
)
learn_trainer = PPOTrainer(actor_critic=expl_policy.learner,
**variant["trainer_kwargs"])
control_trainer = DummyTrainer()
trainer = MultiTrainer([control_trainer, learn_trainer])
replay_buffer = EnvReplayBuffer(variant["replay_buffer_size"], expl_envs)
algorithm = TorchIkostrikovRLAlgorithm(
trainer=trainer,
exploration_env=expl_envs,
evaluation_env=eval_envs,
exploration_data_collector=expl_path_collector,
evaluation_data_collector=eval_path_collector,
replay_buffer=replay_buffer,
**variant["algorithm_kwargs"],
# batch_size,
# max_path_length,
# num_epochs,
# num_eval_steps_per_epoch,
# num_expl_steps_per_train_loop,
# num_trains_per_train_loop,
# num_train_loops_per_epoch=1,
# min_num_steps_before_training=0,
)
algorithm.to(ptu.device)
algorithm.train()