o `۷i)@sbddlmZddlmZmZddlmZddlmZddl m Z e\Z Z eGdddeZ dS) )Optional) OldAPIStackoverride)try_import_torch)Schedule) TensorTypec sXeZdZdZ   ddedeededeffd d Ze e d e d e fd dZ Z S)ExponentialSchedulezExponential decay schedule from `initial_p` to `final_p`. Reduces output over `schedule_timesteps`. After this many time steps always returns `final_p`. N?皙?schedule_timesteps framework initial_p decay_ratecs0tj|d|dks J||_||_||_dS)aGInitializes a ExponentialSchedule instance. Args: schedule_timesteps: Number of time steps for which to linearly anneal initial_p to final_p. framework: The framework descriptor string, e.g. "tf", "torch", or None. initial_p: Initial output value. decay_rate: The percentage of the original value after 100% of the time has been reached (see formula above). >0.0: The smaller the decay-rate, the stronger the decay. 1.0: No decay at all. )r rN)super__init__r r r)selfr r r r __class__d/home/ubuntu/vllm_env/lib/python3.10/site-packages/ray/rllib/utils/schedules/exponential_schedule.pyrs   zExponentialSchedule.__init__treturncCs8|jdkrtrt|tjr|}|j|j||jS)z=Returns the result of: initial_p * decay_rate ** (`t`/t_max).torch)r r isinstanceTensorfloatr rr )rrrrr_value-szExponentialSchedule._value)Nr r )__name__ __module__ __qualname____doc__intrstrrrrrrr __classcell__rrrrr s  rN)typingrray.rllib.utils.annotationsrrray.rllib.utils.frameworkr"ray.rllib.utils.schedules.schedulerray.rllib.utils.typingrr_rrrrrs