double_q_learn_tabular_seq.py

import itertools
import yaml
from collections import OrderedDict

num_seeds = 10

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": [i for i in range(1, 5)],
        "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.
        "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,
        "reward_every_n_steps": True,
    },
}


with open("tabular_rl/config.yaml", "r") as stream:
    config = yaml.safe_load(stream)

env_name = config["env_name"]
agent_name = config["agent_name"]

agent_config = config["agents"][agent_name]

eval_eps = config["eval_eps"]
seed = config["seed"]
no_render = config["no_render"]
discount_factor = config["discount_factor"]
alpha = agent_config["alpha"]

episodes = agent_config["episodes"]
env_max_steps = agent_config["env_max_steps"]
agent_eps_decay = agent_config["agent_eps_decay"]
agent_eps = agent_config["agent_eps"]
# timesteps_per_iteration = agent_config["timesteps_per_iteration"]
agent_config = eval_eps


agent_config = {
    # "env_max_steps": env_max_steps,
    "num_episodes": episodes,
    "epsilon_decay": agent_eps_decay,
    "epsilon": agent_eps,
    "render_eval": no_render,
    "discount_factor": discount_factor,
    "alpha": alpha,
    "eval_every": eval_eps,
    # "timesteps_per_iteration": timesteps_per_iteration, #todo: perhaps pass this later as an argument to the agent
}

algorithm = "double_q_learn_tabular_seq"
# 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,
# }


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,
    },
}
value_tuples = []
for config_type, config_dict in var_configs.items():
    for key in config_dict:
        assert isinstance(
            var_configs[config_type][key], list
        ), "var_config should be a dict of dicts with lists as the leaf values to allow each configuration option to take multiple possible values"
        value_tuples.append(var_configs[config_type][key])


cartesian_product_configs = list(itertools.product(*value_tuples))
print("Total number of configs. to run:", len(cartesian_product_configs))