a3c_beam_rider_del.py

import itertools
from ray import tune
from collections import OrderedDict

num_seeds = 5
timesteps_total = 10_000_000

var_env_configs = OrderedDict(
    {
        "delay": [0] + [2 ** i for i in range(4)],
        "dummy_seed": [i for i in range(num_seeds)],
    }
)

var_configs = OrderedDict({"env": var_env_configs})

env_config = {
    "env": "GymEnvWrapper-Atari",
    "env_config": {
        "AtariEnv": {
            "game": "beam_rider",  # "breakout",
            "obs_type": "image",
            "frameskip": 1,
        },
        # "GymEnvWrapper": {
        "atari_preprocessing": True,
        "frame_skip": 4,
        "grayscale_obs": False,
        "state_space_type": "discrete",
        "action_space_type": "discrete",
        "seed": 0,
        # },
        # 'seed': 0, #seed
    },
}

algorithm = "A3C"
agent_config = {  # Taken from Ray tuned_examples
    "clip_rewards": True,
    "lr": 1e-4,
    # Value Function Loss coefficient
    "vf_loss_coeff": 2.5,
    # Entropy coefficient
    "entropy_coeff": 0.01,
    "min_iter_time_s": 0,
    "num_envs_per_worker": 5,
    "num_gpus": 0,
    "num_workers": 3,
    "rollout_fragment_length": 10,
    "timesteps_per_iteration": 10000,
    # "tf_session_args": {
    #     # note: overriden by `local_tf_session_args`
    #     "intra_op_parallelism_threads": 4,
    #     "inter_op_parallelism_threads": 4,
    #     # "gpu_options": {
    #     #     "allow_growth": True,
    #     # },
    #     # "log_device_placement": False,
    #     "device_count": {
    #         "CPU": 2,
    #         # "GPU": 0,
    #     },
    #     # "allow_soft_placement": True,  # required by PPO multi-gpu
    # },
    # # Override the following tf session args on the local worker
    # "local_tf_session_args": {
    #     "intra_op_parallelism_threads": 4,
    #     "inter_op_parallelism_threads": 4,
    # },
}


model_config = {
    # "model": {
    #     "fcnet_hiddens": [256, 256],
    #     "fcnet_activation": "tanh",
    #     "use_lstm": False,
    #     "max_seq_len": 20,
    #     "lstm_cell_size": 256,
    #     "lstm_use_prev_action_reward": False,
    # },
}


eval_config = {
    "evaluation_interval": None,  # I think this means every x training_iterations
    "evaluation_config": {
        "explore": False,
        "exploration_fraction": 0,
        "exploration_final_eps": 0,
        "evaluation_num_episodes": 10,
        "horizon": 100,
        "env_config": {
            "dummy_eval": True,  # hack Used to check if we are in evaluation mode or training mode inside Ray callback on_episode_end() to be able to write eval stats
            "transition_noise": 0
            if "state_space_type" in env_config["env_config"]
            and env_config["env_config"]["state_space_type"] == "discrete"
            else tune.function(lambda a: a.normal(0, 0)),
            "reward_noise": tune.function(lambda a: a.normal(0, 0)),
            "action_loss_weight": 0.0,
        },
    },
}
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))