def main(args):
"""Run experiment."""
set_random_seed(args.seed)
env_config = parse_config_file(args.env_config_file)
environment = GymEnvironment(
env_config["name"], ctrl_cost_weight=env_config["action_cost"], seed=args.seed
)
reward_model = environment.env.reward_model()
if args.exploration == "optimistic":
dynamical_model = HallucinatedModel.default(environment, beta=args.beta)
environment.add_wrapper(HallucinationWrapper)
else:
dynamical_model = TransformedModel.default(environment)
kwargs = parse_config_file(args.agent_config_file)
agent = getattr(
importlib.import_module("rllib.agent"), f"{args.agent}Agent"
).default(
environment=environment,
dynamical_model=dynamical_model,
reward_model=reward_model,
thompson_sampling=args.exploration == "thompson",
**kwargs,
)
train_agent(
agent=agent,
environment=environment,
max_steps=env_config["max_steps"],
num_episodes=args.train_episodes,
render=args.render,
print_frequency=1,
)
evaluate_agent(
agent=agent,
environment=environment,
max_steps=env_config["max_steps"],
num_episodes=args.test_episodes,
)
"""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")