def __init__(self, config: Optional[EnvContext] = None):
# Override with default values, in case they are not set by the user.
default_config = {
"num_candidates": 10,
"slate_size": 2,
"resample_documents": True,
"seed": 0,
"convert_to_discrete_action_space": False,
"wrap_for_bandits": False,
}
if config is None or isinstance(config, dict):
config = EnvContext(config or default_config, worker_index=0)
config.set_defaults(default_config)
# Create the RecSim user model instance.
recsim_user_model = recsim_user_model_creator(config)
# Create the RecSim document sampler instance.
recsim_document_sampler = recsim_document_sampler_creator(config)
# Create a raw RecSim environment (not yet a gym.Env!).
raw_recsim_env = environment.SingleUserEnvironment(
recsim_user_model,
recsim_document_sampler,
config["num_candidates"],
config["slate_size"],
resample_documents=config["resample_documents"],
)
# Convert raw RecSim env to a gym.Env.
gym_env = recsim_gym.RecSimGymEnv(raw_recsim_env,
reward_aggregator)
# Fix observation space and - if necessary - convert to discrete
# action space (from multi-discrete).
env = recsim_gym_wrapper(
gym_env,
config["convert_to_discrete_action_space"],
config["wrap_for_bandits"],
)
# Call the super (Wrapper constructor) passing it the created env.
super().__init__(env=env)
def __init__(self, env_ctx: Optional[EnvContext] = None):
# Override with default values, in case they are not set by the user.
default_config = {
"num_candidates": 10,
"slate_size": 2,
"resample_documents": True,
"seed": 0,
"convert_to_discrete_action_space": False,
}
if env_ctx is None or isinstance(env_ctx, dict):
env_ctx = EnvContext(env_ctx or default_config, worker_index=0)
env_ctx.set_defaults(default_config)
# Create the RecSim user model instance.
recsim_user_model = recsim_user_model_creator(env_ctx)
# Create the RecSim document sampler instance.
recsim_document_sampler = recsim_document_sampler_creator(env_ctx)
# Create a raw RecSim environment (not yet a gym.Env!).
raw_recsim_env = environment.SingleUserEnvironment(
recsim_user_model,
recsim_document_sampler,
env_ctx["num_candidates"],
env_ctx["slate_size"],
resample_documents=env_ctx["resample_documents"],
)
# Convert raw RecSim env to a gym.Env.
gym_env = recsim_gym.RecSimGymEnv(raw_recsim_env,
reward_aggregator)
# Fix observation space and - if necessary - convert to discrete
# action space (from multi-discrete).
self.env = recsim_gym_wrapper(
gym_env, env_ctx["convert_to_discrete_action_space"])
self.observation_space = self.env.observation_space
self.action_space = self.env.action_space