user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model Imitation for Model-Based Reinforcement Learning

Paper content:
Model-based reinforcement learning (MBRL) aims to learn a dynamic model to reduce the number of interactions with real-world environments.
However, due to estimation error, rollouts in the learned model, especially those of long horizon, fail to match the ones in real-world environments.
This mismatching has seriously impacted the sample complexity of MBRL.
The phenomenon can be attributed to the fact that previous works employ supervised learning to learn the one-step transition models, which has inherent difficulty ensuring the matching of distributions from multi-step rollouts.
Based on the claim, we propose to learn the synthesized model by matching the distributions of multi-step rollouts sampled from the synthesized model and the real ones via WGAN.
We theoretically show that matching the two can minimize the difference of cumulative rewards between the real transition and the learned one.
Our experiments also show that the proposed model imitation method outperforms the state-of-the-art in terms of sample complexity and average return.
Reinforcement learning (RL) has become of great interest because plenty of real-world problems can be modeled as a sequential decision-making problem.
Model-free reinforcement learning (MFRL) is favored by its capability of learning complex tasks when interactions with environments are cheap.
However, in the majority of real-world problems, such as autonomous driving, interactions are extremely costly, thus MFRL becomes infeasible.
One critique about MFRL is that it does not fully exploit past queries over the environment, and this motivates us to consider the model-based reinforcement learning (MBRL).
In addition to learning an agent policy, MBRL also uses the queries to learn the dynamics of the environment that our agent is interacting with.
If the learned dynamic is accurate enough, the agent can acquire the desired skill by simply interacting with the simulated environment, so that the number of samples to collect in the real world can be greatly reduced.
As a result, MBRL has become one of the possible solutions to reduce the number of samples required to learn an optimal policy.
Most previous works of MBRL adopt supervised learning with 2 -based errors (Luo et al., 2019; Kurutach et al., 2018; or maximum likelihood (Janner et al., 2019) , to obtain an environment model that synthesizes real transitions.
These non-trivial developments imply that optimizing a policy on a synthesized environment is a challenging task.
Because the estimation error of the model accumulates as the trajectory grows, it is hard to train a policy on a long synthesized trajectory.
On the other hand, training on short trajectories makes the policy short-sighted.
This issue is known as the planning horizon dilemma (Wang et al., 2019) .
As a result, despite having a strong intuition at first sight, MBRL has to be designed meticulously.
Intuitively, we would like to learn a transition model in a way that it can reproduce the trajectories that have been generated in the real world.
Since the attained trajectories are sampled according to a certain policy, directly employing supervised learning may not necessarily lead to the mentioned result especially when the policy is stochastic.
The resemblance in trajectories matters because we estimate policy gradient by generating rollouts; however, the one-step model learning adopted by many MBRL methods do not guarantee this.
Some previous works propose multi-step training (Luo et al., 2019; Asadi et al., 2019; Talvitie, 2017) ; however, experiments show that model learning fails to benefit much from the multi-step loss.
We attribute this outcome to the essence of super-
We have pointed out that the state-of-the-art methods concentrate on learning synthesized models in a supervised fashion, which does not guarantee that the policy is able to reproduce a similar trajectory in the learned model and therefore the model may not be accurate enough to estimate long rollouts.
We have proposed to incorporate WGAN to achieve occupancy measure matching between the real transition and the synthesized model and theoretically shown that matching indicates the closeness in cumulative rewards between the synthesized model and the real environment.
To enable stable training across WGANs, we have suggested using a truncated version of WGAN to prevent training from getting stuck at local optimums.
The empirical property of WGAN application such as imitation learning indicates its potential to learn the transition with fewer samples than supervised learning.
We have confirmed it experimentally by further showing that MI converges much faster and obtains better policy than state-of-the-art model-based and model-free algorithms.

assistant: text: Our method incorporates WGAN to achieve occupancy measure matching for transition learning.