gae_lambda = 0.95 entropy_coef = 0.01 value_loss_coef = 0.5 max_grad_norm = 0.5 seed = 1 # didnt change cuda_deterministic = False num_processes = 1 num_steps = 2500 custom_gym = "growspace" ppo_epoch = 4 num_mini_batch = 32 clip_param = 0.1 log_interval = 10 # amount of times we save to wandb save_interval = 100 # amount of times we save internal eval_interval = None num_env_steps = 1e6 # no change env_name = "GrowSpaceEnv-Control-v0" #"GrowSpaceSpotlight-Mnist4-v0" log_dir = "/tmp/gym/" save_dir = "./trained_models/" use_proper_time_limits = False recurrent_policy = False use_linear_lr_decay = True no_cuda = False cuda = not no_cuda and torch.cuda.is_available() experiment_buddy.register(locals()) tensorboard = experiment_buddy.deploy("mila", sweep_yaml="pposweep.yaml", proc_num=10, wandb_kwargs={"entity": "growspace"})
use_fa = False horizon = 6 if use_fa else 20 penalty = 0.15 if use_fa else 1.4 #1.6 eps = 1e-4 gamma = 0.9 eta = 1. grid_size = 10 agent = "pg_clip" save_interval = 10 max_steps = int(2e3) seed = 984 eval_episodes = 10 data = "data" REMOTE = 1 RUN_SWEEP = REMOTE NUM_PROCS = 5 sweep_yaml = "sweep_params.yaml" if RUN_SWEEP else False HOST = "mila" if REMOTE else "" # in host DEBUG = '_pydev_bundle.pydev_log' in sys.modules.keys() render = not DEBUG experiment_buddy.register(locals()) tb = experiment_buddy.deploy(host=HOST, sweep_yaml=sweep_yaml, proc_num=NUM_PROCS, wandb_kwargs=dict(mode= "disabled" if DEBUG else "online")) os.makedirs(data, exist_ok=True) plot_path = os.path.join(tb.objects_path, "plots") os.makedirs(plot_path, exist_ok=True)
seed = 984 h_dim = 32 # wandb_mode = "online" if DEBUG else "offline" use_cuda = False experiment_buddy.register(locals()) device = torch.device("cuda" if use_cuda else "cpu") ################################################################ # Derivative parameters ################################################################ # esh = """ # #SBATCH --mem=24GB # """ esh = """ #SBATCH --job-name=spython #SBATCH --output=job_output.txt #SBATCH --error=job_error.txt #SBATCH --time=2-00:00 #SBATCH --mem=12GB #SBATCH --gres=gpu:1 #SBATCH --cpus-per-task=4 #SBATCH --partition=long #SBATCH --get-user-env=L """ tb = experiment_buddy.deploy(host=HOST, sweep_yaml=sweep_yaml, extra_slurm_headers=esh, proc_num=NUM_PROCS)
num_steps = 10000 algo = "ppo" gail = False gail_experts_dir = './gail_experts' gail_batch_size = 128 gail_epoch = 5 alpha = 0.99 seed = np.random.randint(1, 80, size=1) # didnt change cuda_deterministic = False num_processes = 4 custom_gym = "growspace" log_interval = 10 save_interval = 100 eval_interval = None num_updates = 1e3 env_name = "GrowSpaceEnv-Control-v0" # "GrowSpaceSpotlight-MnistMix-v0" log_dir = "/tmp/gym/" save_dir = "./trained_models/" use_proper_time_limits = False recurrent_policy = False no_cuda = False cuda = not no_cuda and torch.cuda.is_available() momentum = 0.9 # if sgd is used experiment_buddy.register(locals()) tensorboard = experiment_buddy.deploy("", sweep_yaml="", proc_num=3, wandb_kwargs={"entity": "growspace"})
import experiment_buddy # Hyperparameters ENV_NAME = 'BipedalWalker-v3' MAX_ITER = 500000 BATCH_SIZE = 64 PPO_EPOCHS = 7 CLIP_GRADIENT = 0.2 CLIP_EPS = 0.2 TRAJECTORY_SIZE = 2049 GAE_LAMBDA = 0.95 GAMMA = 0.99 ## Test Hyperparameters test_episodes = 50 save_video_test = False N_ITER_TEST = 100 POLICY_LR = 0.0004 VALUE_LR = 0.001 experiment_buddy.register(locals()) tensorboard = experiment_buddy.deploy("", sweep_yaml="", proc_num=1, wandb_kwargs={"entity": "ionelia"})
import experiment_buddy initial_lr = .0001 decay_steps = 500000 num_hidden = 1024 decay_factor = .5 batch_size = 128 momentum_mass = 0.99 weight_norm = 0.00 num_epochs = 1000 experiment_buddy.register(locals()) ################################################################ # Derivative parameters ################################################################ learning_rate = jax.experimental.optimizers.inverse_time_decay(initial_lr, decay_steps, decay_factor, staircase=True) eval_every = math.ceil(num_epochs / 1000) HOST = os.environ['DEPLOY_DESTINATION'] host_map = {'cluster': 'mila', 'local': ''} tensorboard = experiment_buddy.deploy(host=host_map[HOST], sweep_yaml="")
import jax.experimental.optimizers import experiment_buddy initial_lr = .0001 decay_steps = 500000 num_hidden = 1024 decay_factor = .5 batch_size = 128 momentum_mass = 0.99 weight_norm = 0.00 num_epochs = 1000 experiment_buddy.register(locals()) ################################################################ # Derivative parameters ################################################################ learning_rate = jax.experimental.optimizers.inverse_time_decay(initial_lr, decay_steps, decay_factor, staircase=True) eval_every = math.ceil(num_epochs / 1000) tensorboard = experiment_buddy.deploy(host=os.environ.get('BUDDY_HOST', ""), sweep_yaml=os.environ.get('SWEEP', ""))