In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition. Various methods based on variational auto-encoder have been proposed to solve this problem, by enforcing the independence between the representation and modifying the regularization term in the variational lower bound. However recent work by Locatello et al. (2018) has demonstrated that the proposed methods are heavily influenced by randomness and the choice of the hyper-parameter. This work is built upon the same framework in Stage 1 (Li et al., 2019), but with different settings; to make it self-contained, we provide this manuscript, which is unavoidably very similar to the report for Stage 1. In detail, in this work, instead of designing a new regularization term, we adopt the FactorVAE but improve the reconstruction performance and increase the capacity of network and the training step. The strategy turns out to be very effective in achieving disentanglement.
[A]: disentangled representation learning

[Q]: Deep reinforcement learning (RL) policies are known to be vulnerable to adversarial perturbations to their observations, similar to adversarial examples for classifiers. However, an attacker is not usually able to directly modify another agent's observations. This might lead one to wonder: is it possible to attack an RL agent simply by choosing an adversarial policy acting in a multi-agent environment so as to create natural observations that are adversarial? We demonstrate the existence of adversarial policies in zero-sum games between simulated humanoid robots with proprioceptive observations, against state-of-the-art victims trained via self-play to be robust to opponents. The adversarial policies reliably win against the victims but generate seemingly random and uncoordinated behavior. We find that these policies are more successful in high-dimensional environments, and induce substantially different activations in the victim policy network than when the victim plays against a normal opponent. Videos are available at https://attackingrl.github.io.
[A]: Deep RL policies can be attacked by other agents taking actions so as to create natural observations that are adversarial.

[Q]: Board games often rely on visual information such as the location of the game pieces and textual information on cards. Due to this reliance on visual feedback, blind players are at a disadvantage because they cannot read the cards or see the location of the game pieces and may be unable to play a game without sighted help. We present Game Changer, an augmented workspace that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players. In this paper, we describe the design of Game Changer and present findings from a user study in which 7 blind participants used Game Changer to play against a sighted partner. Most players stated the game was more accessible with the additions from Game Changer and felt that Game Changer could be used to augment other games.
[A]:
Game Changer is a system that provides both audio descriptions and tactile additions to make the state of the board game accessible to blind and visually impaired players.