def run(hidden_dim=256):
path = os.getcwd()+"/nh_waypoint_3d/"
fname = path+"gaussian_2_term.pth.tar"
t_env = tenv.WaypointEnv3D()
state_dim = t_env.observation_space.shape[0]
action_dim = t_env.action_space.shape[0]
agent = ag.Agent(state_dim, hidden_dim, action_dim, dim=3)
print("Agent initialized, loading state dictionary.")
agent.load_state_dict(torch.load(fname, map_location=lambda storage, loc: storage))
print()
print("State dictionary loaded")
k = [tl.test(t_env, agent, render=True) for _ in range(100)]
rewards = sum(k)/len(k)
print("Mean reward: ", rewards)
def run(hidden_dim=256):
path = os.getcwd() + "/_2d/soft_2d/"
fname = path + "3-wps_.pth.tar"
t_env = tenv.TrajectoryEnv2D()
state_dim = t_env.observation_space.shape[0]
action_dim = t_env.action_space.shape[0]
agent = ag.Agent(state_dim, hidden_dim, action_dim, dim=2, lookahead=3)
print("Agent initialized, loading state dictionary.")
agent.load_state_dict(
torch.load(fname, map_location=lambda storage, loc: storage))
print()
print("State dictionary loaded")
k = [tl.test(t_env, agent, render=True) for _ in range(100)]
rewards = sum(k) / len(k)
print("Mean reward: ", rewards)