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Title: Reward Estimation via State Prediction

Paper content:
Reinforcement learning typically requires carefully designed reward functions in order to learn the desired behavior.
We present a novel reward estimation method that is based on a finite sample of optimal state trajectories from expert demon- strations and can be used for guiding an agent to mimic the expert behavior.
The optimal state trajectories are used to learn a generative or predictive model of the “good” states distribution.
The reward signal is computed by a function of the difference between the actual next state acquired by the agent and the predicted next state given by the learned generative or predictive model.
With this inferred reward function, we perform standard reinforcement learning in the inner loop to guide the agent to learn the given task.
Experimental evaluations across a range of tasks demonstrate that the proposed method produces superior performance compared to standard reinforcement learning with both complete or sparse hand engineered rewards.
Furthermore, we show that our method successfully enables an agent to learn good actions directly from expert player video of games such as the Super Mario Bros and Flappy Bird.
Reinforcement learning (RL) deals with learning the desired behavior of an agent to accomplish a given task.
Typically, a scalar reward signal is used to guide the agent's behavior and the agent learns a control policy that maximizes the cumulative reward over a trajectory, based on observations.
This type of learning is referred to as "model-free" RL since the agent does not know apriori or learn the dynamics of the environment.
Although the ideas of RL have been around for a long time BID24 ), great achievements were obtained recently by successfully incorporating deep models into them with the recent success of deep reinforcement learning.
Some notable breakthroughs amongst many recent works are, the work from BID12 who approximated a Q-value function using as a deep neural network and trained agents to play Atari games with discrete control; who successfully applied deep RL for continuous control agents achieving state of the art; and BID22 who formulated a method for optimizing control policies with guaranteed monotonic improvement.In most RL methods, it is very critical to choose a well-designed reward function to successfully learn a good action policy for performing the task.
However, there are cases where the reward function required for RL algorithms is not well-defined or is not available.
Even for a task for which a reward function initially seems to be easily defined, it is often the case that painful hand-tuning of the reward function has to be done to make the agent converge on an optimal behavior.
This problem of RL defeats the benefits of automated learning.
In contrast, humans often can imitate instructor's behaviors, at least to some extent, when accomplishing a certain task in the real world, and can guess what actions or states are good for the eventual accomplishment, without being provided with the detailed reward at each step.
For example, children can learn how to write letters by imitating demonstrations provided by their teachers or other adults (experts).
Taking inspiration from such scenarios, various methods collectively referred to as imitation learning or learning from experts' demonstrations have been proposed BID21 ) as a relevant technical branch of RL.
Using these methods, expert demonstrations can be given as input to the learning algorithm.
Inverse reinforcement learning BID15 ; BID1 ; BID28 ), behavior cloning BID20 ), imitation learning BID6 ; BID5 ), and curiosity-based exploration ) are examples of research in this direction.While most of the prior work using expert demonstrations assumes that the demonstration trajectories contain both the state and action information (τ = {(s t )}) to solve the imitation learning problem, we, however, believe that there are many cases among real world environments where action information is not readily available.
For example, a human teacher cannot tell the student what amount of force to put on each of the fingers when writing a letter.As such, in this work, we propose a reward estimation method that can estimate the underlying reward based only on the expert demonstrations of state trajectories for accomplishing a given task.
The estimated reward function can be used in RL algorithms in order to learn a suitable policy for the task.
The proposed method has the advantage of training agents based only on visual observations of experts performing the task.
For this purpose, it uses a model of the distribution of the expert state trajectories and defines the reward function in a way that it penalizes the agent's behavior for actions that cause it to deviate from the modeled distribution.
We present two methods with this motivation; a generative model and a temporal sequence prediction model.
The latter defines the reward function by the similarity between the state predicted by the temporal sequence model trained based on the expert's demonstrations and the currently observed state.
We present experimental results of the methods on multiple environments and with varied settings of input and output.
The primary contribution of this paper is in the estimation of the reward function based on state similarity to expert demonstrations, that can be measured even from raw video input.
In this paper, we proposed two variations of a reward estimation method via state prediction by using state-only trajectories of the expert; one based on an autoencoder-based generative model and one based on temporal sequence prediction using LSTM.
Both the models were for calculating similarities between actual states and predicted states.
We compared the methods with conventional reinforcement learning methods in five various environments.
As overall trends, we found that the proposed method converged faster than using hand-crafted reward in many cases, especially when the expert trajectories were given by humans, and also that the temporal sequence prediction model had better results than the generative model.
It was also shown that the method could be applied to the case where the demonstration was given by videos.
However, detailed trends were different for the different environments depending on the complexity of the tasks.
Neither model of our proposed method was versatile enough to be applicable to every environment without any changes of the reward definition.
As we saw in the necessity of the energy term of the reward for the reacher task and in the necessity of special handling of the initial position of Mario, the proposed method has a room of improvements especially in modeling global temporal characteristics of trajectories.
We would like to tackle these problems as future work.

assistant: text: Reward Estimation from Game Videos