def load(dir):
with open(f"{dir}/planner.pkl", 'rb') as f:
planner = pickle.load(f)
planner.world_model = WorldModel.load(dir)
return planner
pickle.dump(transformer, f)
n_obs = transformer.rbf_dim if transformer else env.observation_space.shape[
0]
n_action = env.action_space.shape[0]
max_action = float(env.action_space.high[0])
critic = build_critic(n_obs, args.learning_rate)
if transformer:
critic = NetworkWithTransformer(critic, transformer)
# Initialize agent
if args.agent == "PIP":
copy_tree(f"world_models/{args.world_model}",
f"{model_path}/world_model")
world_model = WorldModel.load(model_path)
planner = PSOPlanner(
env.action_space,
world_model,
critic,
length=args.plan_length,
gamma=args.gamma,
particles=args.particles,
iterations=args.iterations,
c1=args.c1,
c2=args.c2,
w=args.w,
k=args.k,
)
agent = PIP(planner, critic, gamma=args.gamma)
