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default_config.py
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from mdp_playground.config_processor import *
timesteps_total = 10_000
num_seeds = 3
from collections import OrderedDict
var_env_configs = OrderedDict({
'state_space_size': [8],#, 10, 12, 14] # [2**i for i in range(1,6)]
'action_space_size': [8],#2, 4, 8, 16] # [2**i for i in range(1,6)]
'delay': [0] + [2**i for i in range(4)],
'sequence_length': [1, 2, 3, 4],#i for i in range(1,4)]
'reward_density': [0.25], # np.linspace(0.0, 1.0, num=5)
'make_denser': [False],
'terminal_state_density': [0.25], # np.linspace(0.1, 1.0, num=5)
'transition_noise': [0],#, 0.01, 0.02, 0.10, 0.25]
'reward_noise': [0],#, 1, 5, 10, 25] # Std dev. of normal dist.
# 'reward_scale': [10.0],
'dummy_seed': [i for i in range(num_seeds)],
})
var_configs = OrderedDict({
"env": var_env_configs
})
env_config = {
"env": "RLToy-v0",
"horizon": 100,
"env_config": {
'seed': 0, #seed
'state_space_type': 'discrete',
'action_space_type': 'discrete',
'generate_random_mdp': True,
'repeats_in_sequences': False,
'reward_scale': 1.0,
'completely_connected': True,
},
}
eval_config = {}
algorithm = "DQN"
agent_config = {
"adam_epsilon": 1e-4,
"beta_annealing_fraction": 1.0,
"buffer_size": 1000000,
"double_q": False,
"dueling": False,
"exploration_final_eps": 0.01,
"exploration_fraction": 0.1,
"final_prioritized_replay_beta": 1.0,
"hiddens": None,
"learning_starts": 1000,
"lr": 1e-4, # "lr": grid_search([1e-2, 1e-4, 1e-6]),
"n_step": 1,
"noisy": False,
"num_atoms": 1,
"prioritized_replay": False,
"prioritized_replay_alpha": 0.5,
"sample_batch_size": 4,
"schedule_max_timesteps": 20000,
"target_network_update_freq": 800,
"timesteps_per_iteration": 1000,
"min_iter_time_s": 0,
"train_batch_size": 32,
# "use_pytorch": True,
}
model_config = {
"model": {
"fcnet_hiddens": [256, 256],
"custom_preprocessor": "ohe",
"custom_options": {}, # extra options to pass to your preprocessor
"fcnet_activation": "tanh",
"use_lstm": False,
"max_seq_len": 20,
"lstm_cell_size": 256,
"lstm_use_prev_action_reward": False,
},
}