More Like this

1. Rotation, Translation, and Cropping for Zero-Shot Generalization

   https://doi.org/10.1109/CoG47356.2020.9231907  

   Ye, Chang; Khalifa, Ahmed; Bontrager, Philip; Togelius, Julian (August 2020, IEEE Conference on Games)

   null (Ed.)

   Deep Reinforcement Learning (DRL) has shown im- pressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rotation, cropping and translation could increase generality. We show that a cropped, translated and rotated observation can get better generalization on unseen levels of two-dimensional arcade games from the GVGAI framework. The generality of the agents is evaluated on both human-designed and procedurally generated levels. 

   more » « less
2. When Is Generalizable Reinforcement Learning Tractable?

   Malik, D.; Li, Y.; Ravikumar, P. (December 2021, Advances in Neural Information Processing Systems (NeurIPS) 34, 2021)

   Agents trained by reinforcement learning (RL) often fail to generalize beyond the environment they were trained in, even when presented with new scenarios that seem similar to the training environment. We study the query complexity required to train RL agents that generalize to multiple environments. Intuitively, tractable generalization is only possible when the environments are similar or close in some sense. To capture this, we introduce Weak Proximity, a natural structural condition that requires the environments to have highly similar transition and reward functions and share a policy providing optimal value. Despite such shared structure, we prove that tractable generalization is impossible in the worst case. This holds even when each individual environment can be efficiently solved to obtain an optimal linear policy, and when the agent possesses a generative model. Our lower bound applies to the more complex task of representation learning for the purpose of efficient generalization to multiple environments. On the positive side, we introduce Strong Proximity, a strengthened condition which we prove is sufficient for efficient generalization. 

   more » « less
3. Influencing Reinforcement Learning through Natural Language Guidance

   https://doi.org/10.32473/flairs.v34i1.128472  

   Tasrin, Tasmia; Nahian, Md Sultan; Perera, Habarakadage; Harrison, Brent (May 2021, The International FLAIRS Conference Proceedings)

   null (Ed.)

   Interactive reinforcement learning (IRL) agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that’s either positive or negative. While informative, this information can be difficult to generalize on its own. In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known IRL technique. Usually policy shaping employs a human feedback policy to help an agent to learn more about how to achieve its goal. In our case, we replace this human feedback policy with policy generated based on natural language advice. We aim to inspect if the generated natural language reasoning provides support to a deep RL agent to decide its actions successfully in any given environment. So, we design our model with three networks: first one is the experience driven, next is the advice generator and third one is the advice driven. While the experience driven RL agent chooses its actions being influenced by the environmental reward, the advice driven neural network with generated feedback by the advice generator for any new state selects its actions to assist the RL agent to better policy shaping. 

   more » « less
4. Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning

   https://doi.org/10.24963/ijcai.2019/373  

   Juliani, Arthur; Khalifa, Ahmed; Berges, Vincent-Pierre; Harper, Jonathan; Teng, Ervin; Henry, Hunter; Crespi, Adam; Togelius, Julian; Lange, Danny (August 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)

   The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment. An agent in Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal.Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level. 

   more » « less
5. The Impact of Batch Deep Reinforcement Learning on Student Performance: A Simple Act of Explanation Can Go A Long Way

   Sanz_Ausin, Markel; Maniktala, Mehak; Barnes, Tiffany; Chi, Min (April 2023, International journal of artificial intelligence in education)

   While Reinforcement learning (RL), especially Deep RL (DRL), has shown outstanding performance in video games, little evidence has shown that DRL can be successfully applied to human-centric tasks where the ultimate RL goal is to make the \textit{human-agent interactions} productive and fruitful. In real-life, complex, human-centric tasks, such as education and healthcare, data can be noisy and limited. Batch RL is designed for handling such situations where data is \textit{limited yet noisy}, and where \textit{building simulations is challenging}. In two consecutive empirical studies, we investigated Batch DRL for pedagogical policy induction, to choose student learning activities in an Intelligent Tutoring System. In Fall 2018 (F18), we compared the Batch DRL policy to an Expert policy, but found no significant difference between the DRL and Expert policies. In Spring 2019 (S19), we augmented the Batch DRL-induced policy with \textit{a simple act of explanation} by showing a message such as \textit{"The AI agent thinks you should view this problem as a Worked Example to learn how some new rules work."}. We compared this policy against two conditions, the Expert policy, and a student decision making policy. Our results show that 1) the Batch DRL policy with explanations significantly improved student learning performance more than the Expert policy; and 2) no significant differences were found between the Expert policy and student decision making. Overall, our results suggest that \textit{pairing simple explanations with the Batch DRL policy} can be an important and effective technique for applying RL to real-life, human-centric tasks. 

   more » « less