def run_gym(
params: OpenAiGymParameters,
score_bar,
embed_rl_dataset: RLDataset,
gym_env: Env,
mdnrnn: MemoryNetwork,
max_embed_seq_len: int,
):
assert params.rl is not None
rl_parameters = params.rl
env_type = params.env
model_type = params.model_type
epsilon, epsilon_decay, minimum_epsilon = create_epsilon(
offline_train=True, rl_parameters=rl_parameters, params=params
)
replay_buffer = OpenAIGymMemoryPool(params.max_replay_memory_size)
for row in embed_rl_dataset.rows:
replay_buffer.insert_into_memory(**row)
assert replay_buffer.memory_buffer is not None
state_mem = replay_buffer.memory_buffer.state
state_min_value = torch.min(state_mem).item()
state_max_value = torch.max(state_mem).item()
state_embed_env = StateEmbedGymEnvironment(
gym_env, mdnrnn, max_embed_seq_len, state_min_value, state_max_value
)
open_ai_env = OpenAIGymEnvironment(
state_embed_env,
epsilon,
rl_parameters.softmax_policy,
rl_parameters.gamma,
epsilon_decay,
minimum_epsilon,
)
rl_trainer = create_trainer(params, open_ai_env)
rl_predictor = create_predictor(
rl_trainer, model_type, params.use_gpu, open_ai_env.action_dim
)
assert (
params.run_details.max_steps is not None
and params.run_details.offline_train_epochs is not None
), "Missing data required for offline training: {}".format(str(params.run_details))
return train_gym_offline_rl(
gym_env=open_ai_env,
replay_buffer=replay_buffer,
model_type=model_type,
trainer=rl_trainer,
predictor=rl_predictor,
test_run_name="{} offline rl state embed".format(env_type),
score_bar=score_bar,
max_steps=params.run_details.max_steps,
avg_over_num_episodes=params.run_details.avg_over_num_episodes,
offline_train_epochs=params.run_details.offline_train_epochs,
num_batch_per_epoch=None,
)
def run_gym(
params,
use_gpu,
score_bar,
embed_rl_dataset: RLDataset,
gym_env: Env,
mdnrnn: MemoryNetwork,
max_embed_seq_len: int,
):
rl_parameters = RLParameters(**params["rl"])
env_type = params["env"]
model_type = params["model_type"]
epsilon, epsilon_decay, minimum_epsilon = create_epsilon(
offline_train=True, rl_parameters=rl_parameters, params=params
)
replay_buffer = OpenAIGymMemoryPool(params["max_replay_memory_size"])
for row in embed_rl_dataset.rows:
replay_buffer.insert_into_memory(**row)
state_mem = torch.cat([m[0] for m in replay_buffer.replay_memory])
state_min_value = torch.min(state_mem).item()
state_max_value = torch.max(state_mem).item()
state_embed_env = StateEmbedGymEnvironment(
gym_env, mdnrnn, max_embed_seq_len, state_min_value, state_max_value
)
open_ai_env = OpenAIGymEnvironment(
state_embed_env,
epsilon,
rl_parameters.softmax_policy,
rl_parameters.gamma,
epsilon_decay,
minimum_epsilon,
)
rl_trainer = create_trainer(
params["model_type"], params, rl_parameters, use_gpu, open_ai_env
)
rl_predictor = create_predictor(
rl_trainer, model_type, use_gpu, open_ai_env.action_dim
)
return train_gym_offline_rl(
open_ai_env,
replay_buffer,
model_type,
rl_trainer,
rl_predictor,
"{} offline rl state embed".format(env_type),
score_bar,
max_steps=params["run_details"]["max_steps"],
avg_over_num_episodes=params["run_details"]["avg_over_num_episodes"],
offline_train_epochs=params["run_details"]["offline_train_epochs"],
bcq_imitator_hyper_params=None,
)
