reward_threshold (float) – Gym environment argument, the reward threshold before the task is considered solved 
Just from that one sentence definition, it sounds like a total reward that e.g. an agent must earn, before the task is complete, and so ends.
If this were for example a task for the cartpole agent to stay upright/vertical, it might be formulated as the number of frames, so 1 frame = 1 point reward and if
reward_threshold = 200, the agent must balance the pole for 200 frames to succeed.
Have a look at the example of cartpole on the OpenAI Gym website:
candidate_model = model.symmetric_mutate()
rewards = [run_one_episode(env, candidate_model, False) for _ in range(5)]
reward = np.mean(rewards)
if reward >= env.spec.reward_threshold:
print "Reached reward threshold!"
rewards2 = [run_one_episode(env, candidate_model, False) for _ in range(env.spec.trials)]
if np.mean(rewards2) >= env.spec.reward_threshold:
print "Oops, guess it was a fluke"
So the agent runs for 5 episodes (
for _ in range(5)) and each episode returns an award. We compute the mean reward over 5 episodes (
reward = np.mean(rewards)) and then introduce the desired control flow, based on that result.
In this case, they use the default reward_threshold from the environment (
env.spec.reward_threshold), print a success message, and do a final check to see if the agent really has learnt something consistent or not, by checking once again on a new set of episode (
for _ in range(env.spec.trials)).