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Title: Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

Paper content:
Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels.
However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system.
Using the Atari game Breakout, we demonstrate the difficulty of a trained agent in adjusting to simple modifications in the raw image, ones that a human could adapt to trivially.
In transfer learning, the goal is to use the knowledge gained from the source task to make the training of the target task faster and better.
We show that using various forms of fine-tuning, a common method for transfer learning, is not effective for adapting to such small visual changes.
In fact, it is often easier to re-train the agent from scratch than to fine-tune a trained agent.
We suggest that in some cases transfer learning can be improved by adding a dedicated component whose goal is to learn to visually map between the known domain and the new one.
Concretely, we use Unaligned Generative Adversarial Networks (GANs) to create a mapping function to translate images in the target task to corresponding images in the source task.
These mapping functions allow us to transform between various variations of the Breakout game, as well as between different levels of a Nintendo game, Road Fighter.
We show that learning this mapping is substantially more efficient than re-training.
A visualization of a trained agent playing Breakout and Road Fighter, with and without the GAN transfer, can be seen in \url{https://streamable.com/msgtm} and \url{https://streamable.com/5e2ka}.
Transferring knowledge from previous occurrences to new circumstances is a fundamental human capability and is a major challenge for deep learning applications.
A plausible requirement for artificial general intelligence is that a network trained on one task can reuse existing knowledge instead of learning from scratch for another task.
For instance, consider the task of navigation during different hours of the day.
A human that knows how to get from one point to another on daylight will quickly adjust itself to do the same task during night time, while for a machine learning system making a decision based on an input image it might be a harder task.
That is because it is easier for us to make analogies between similar situations, especially in the things we see, as opposed to a robot that does not have this ability and its knowledge is based mainly on what it already saw.Deep reinforcement learning has caught the attention of researchers in the past years for its remarkable success in achieving human-level performance in a wide variety of tasks.
One of the field's famous achievements was on the Atari 2600 games where an agent was trained to play video games directly from the screen pixels and information received from the game BID20 .
However, this approach depends on interacting with the environment a substantial number of times during training.
Moreover, it struggles to generalize beyond its experience, the training process of a new task has to be performed from scratch even for a related one.
Recent works have tried to overcome this inefficiency with different approaches such as, learning universal policies that can generalize between related tasks BID25 , as well as other transfer approaches BID7 BID24 .
In this work, we first focus on the Atari game Breakout, in which the main concept is moving the paddle towards the ball in order to maximize the score of the game.
We modify the game by introducing visual changes such as adding a rectangle in the middle of the image or diagonals in the background.
From a human perspective, it appears that making visual changes that are not significant to the game's dynamics should not influence the score of the game, a player who mastered the original game should be able to trivially adapt to such visual variants.
We show that the agent fails to transfer.
Furthermore, fine-tuning, the main transfer learning method used today in neural networks, also fails to adapt to the small visual change: the information learned in the source task does not benefit the learning process of the very related target task, and can even decelerate it.
The algorithm behaves as if these are entirely new tasks.Our second focus is attempting to transfer agent behavior across different levels of a video game: can an agent trained on the first level of a game use this knowledge and perform adequately on subsequent levels?
We explore the Nintendo game Road Fighter, a car racing game where the goal is to finish the track before the time runs out without crashing.
The levels all share the same dynamics, but differ from each other visually and in aspects such as road width.
Similar to the Breakout results, an agent trained to play the first level fails to correctly adapt its past experience, causing the learned policy to completely fail on the new levels.To address the generalization problem, we propose a zero-shot generalization approach, in which the agent learns to transfer between related tasks by learning to visually map images from the target task back to familiar corresponding images from the source task.
Such mapping is naturally achieved using Generative Adversarial Networks (GANs) BID9 , one of the most popular methods for the image-to-image translation that is being used in computer vision tasks such as style transfer BID15 , object transfiguration BID31 , photo enhancement BID17 and more recently, video game level generation BID27 .
In our setup, it is not realistic to assume paired images in both domains, calling for the use of Unaligned GANs BID19 BID15 .
Using this approach we manage to transfer between similar tasks with no additional learning.Contributions This work presents three main contributions.
First, in Section 2, we demonstrate how an agent trained with deep reinforcement learning algorithms fails to adapt to small visual changes, and that the common transfer method of fine-tuning fails as well.
Second, in Section 3, we propose to separate the visual mapping from the game dynamics, resulting in a new transfer learning approach for related tasks based on visual input mapping.
We evaluate this approach on Breakout and Road Fighter, and present the results comparing to different baselines.
We show that our visual transfer approach is much more sample efficient then the alternatives.
Third, in section 5, we suggest an evaluation setup for unaligned GAN architectures, based on their achieved performance on concrete down-stream tasks.
We demonstrated the lack of generalization by looking at artificially constructed visual variants of a game (Breakout), and different levels of a game (Road Fighter).
We further show that transfer learning by fine-tuning fails.
The policies learned using model-free RL algorithms on the original game are not directly transferred to the modified games even when the changes are irrelevant to the game's dynamics.We present a new approach for transfer learning between related RL environments using GANs without the need for any additional training of the RL agent, and while requiring orders of magnitude less interactions with the environment.
We further suggest this setup as a way to evaluate GAN architectures by observing their behavior on concrete tasks, revealing differences between the Cycle-GAN and UNIT-GAN architectures.
We believe our approach is applicable to cases involving both direct and less direct mapping between environments, as long as an image-to-image translation exist.
While we report a success in analogy transfer using Unaligned GANs, we also encountered limitations in the generation process that made it difficult for the agent to maximize the results on the Road Fighter's tasks.
In future work, we plan to explore a tighter integration between the analogy transfer method and the RL training process, to facilitate better performance where dynamic adjustments are needed in addition to the visual mapping.

assistant: text: We propose a method of transferring knowledge between related RL tasks using visual mappings, and demonstrate its effectiveness on visual variants of the Atari Breakout game and different levels of Road Fighter, a Nintendo car driving game.