Can not change learning rate after initialization

#5536 # System information

• **OS Platform and Distribution : Ubuntu 18.04
• Ray installed from (source or binary): binary
• Ray version: 0.7.3
• Python version: 3.6.8
• Exact command to reproduce:

I’m trying to change learning rate of policies in training loop,

while True
      rest=trainer.train()
     #here I want to change learning rate based on environment statistics

I tried to use reset_config function, but it doesn’t work

def gen_policy(GENV,lr):
    config = {
        "model": {
            "custom_model": 'GomokuModel',
            "custom_options": {"use_symmetry": True, "reg_loss": 0},
        },
        "cur_lr":lr,
        "custom_action_dist": Categorical,
    }
    return (None, GENV.observation_space, GENV.action_space, config)

new_config = trainer.get_config()
new_config['multi_agent']['policy_0']=gen_policy(GENV,0.0123)
resss=trainer.reset_config(new_config)

[yarik1988]

It seems the best choice is do such way if you want to change some hyper-parameters

state = trainer.save()
trainer.stop()
#re_initialise trainer
trainer.restore(state)

[kiddyboots216]

Hi, can you try this? I’ve been able to change the LR of a PPO policy in this manner. Keep in mind that the policy you’re using almost certainly has an LR schedule, so that’s why I end up setting the schedule with the value that I want.