def run_experiment(agents, environment, args, seed=None):
"""Run a set of experiments."""
seed = args.seed if seed is None else seed
df = pd.DataFrame()
for name, agent in agents.items():
set_random_seed(seed)
print(f"Running agent {name} on {args.env_name}")
evaluate_policy(agent, environment)
store_value_function(agent, environment, episode=0)
rollout_agent(
agent=agent,
environment=environment,
num_episodes=args.num_episodes,
max_steps=args.max_steps,
callback_frequency=1,
callbacks=[evaluate_policy, store_value_function],
)
df_ = pd.DataFrame(agent.logger.statistics)
df_["name"] = name
df_["seed"] = args.seed
df_["time"] = np.arange(len(df_))
df_["duals"] = np.empty((len(df_)), dtype=object)
duals = agent.logger.all["dual_loss"]
for i in range(len(df_)):
df_.at[i, "duals"] = duals[i * args.num_iter : (i + 1) * args.num_iter]
df = pd.concat((df, df_), sort=False)
return df
def init_experiment(args, **kwargs):
"""Initialize experiment."""
arg_dict = vars(args)
arg_dict.update(kwargs)
arg_dict.update(parse_config_file(args.agent_config))
arg_dict = {k: v for k, v in arg_dict.items() if v is not None}
env_config = parse_config_file(args.env_config)
args.max_steps = env_config.get("max_steps", 1000)
# %% Set Random seeds.
set_random_seed(args.seed)
# %% Initialize environment.
if env_config["name"] in gym_envs:
environment = GymEnvironment(env_config["name"], seed=args.seed)
else:
env_name, env_task = env_config["name"].split("/")
environment = DMSuiteEnvironment(env_name, env_task, seed=args.seed)
# %% Initialize module.
agent_module = importlib.import_module("rllib.agent")
agent = getattr(agent_module, f"{args.agent}Agent").default(
environment,
reward_transformer=RewardTransformer(
scale=arg_dict.get("reward_scale", 1.0)),
**arg_dict,
)
agent.logger.save_hparams(arg_dict)
return agent, environment
"""Python Script Template."""
from rllib.environment import GymEnvironment
from rllib.util.utilities import set_random_seed
from qreps.environment.random_action_wrapper import RandomActionWrapper
import os
from exps.utilities import parse_arguments, run_experiment
from exps.environments.utilities import get_saddle_agents, get_benchmark_agents
args = parse_arguments()
args.env_name = "RiverSwim-v0"
args.lr = 0.01
args.eta = 5.0
set_random_seed(args.seed)
env = GymEnvironment(args.env_name, seed=args.seed)
env.add_wrapper(RandomActionWrapper, p=args.random_action_p)
agents = get_saddle_agents(env, **vars(args))
agents.update(get_benchmark_agents(env, **vars(args)))
df = run_experiment(agents, env, args)
df.to_pickle(f"river_swim_results_{args.seed}.pkl")
os.system("python river_swim_plot.py")
