"batch_size": 128,
"buffer_size": 100000,
"epsilon": 1.0,
"epsilon_decay_rate_denominator": 150,
"discount_rate": 0.999,
"alpha_prioritised_replay": 0.6,
"beta_prioritised_replay": 0.1,
"incremental_td_error": 1e-8,
"update_every_n_steps": 15,
"tau": 1e-2,
"linear_hidden_units": [256, 256],
"final_layer_activation": "softmax",
# "y_range": (-1, 14),
"batch_norm": False,
"gradient_clipping_norm": 5,
"HER_sample_proportion": 0.8,
"learning_iterations": 1,
"clip_rewards": False
}
}
config.model = FCNN()
if __name__== '__main__':
AGENTS = [DQN, DRQN, ]#DDQN, Dueling_DDQN, DDQN_With_Prioritised_Experience_Replay]
trainer = Trainer(config, AGENTS)
trainer.train()
import wandb
from gym.core import Wrapper
from torch.cuda import is_available
config = Config()
config.environment = Wrapper(SimpleISC(mode="DISCRETE"))
config.num_episodes_to_run = 5_000
config.file_to_save_data_results = "results/data_and_graphs/isc/IllinoisSolarCar_Results_Data.pkl"
config.runs_per_agent = 1
config.use_GPU = is_available()
config.overwrite_existing_results_file = True
config.randomise_random_seed = False
config.save_model = False
config.model = None
config.seed = 0
config.debug_mode = True
config.wandb_log = True
config.wandb_job_type = "testing"
config.wandb_entity = "rafael_piacsek"
config.wandb_tags = ["initial testing"]
config.wandb_model_log_freq = 1_000
config.hyperparameters = dict(
# y_range=(-1, 14),
HER_sample_proportion=0.8,
alpha_prioritised_replay=0.6,
batch_norm=False,
batch_size=64,
