# MIT License

# Copyright (c) 2023 Replicable-MARL

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# ray/rllib config

local_mode: True # True for debug mode only
share_policy: "group" #  individual(separate) / group(division) / all(share)
evaluation_interval: 50 # evaluate model every 10 training iterations
framework: "torch"
num_workers: 1 # thread number
num_gpus: 1 # gpu to use
num_cpus_per_worker: 1 # cpu allocate to each worker
num_gpus_per_worker: 0 # gpu allocate to each worker
checkpoint_freq: 100 # save model every 100 training iterations
checkpoint_end: True # save model at the end of the exp
restore_path: {"model_path": "", "params_path": ""} # load model and params path: 1. resume exp 2. rendering policy
stop_iters: 9999999 # stop training at this iteration
stop_timesteps: 2000000 # stop training at this timesteps
stop_reward: 999999 # stop training at this reward
seed: 321 # ray seed
local_dir: "" #  all results placed