We propose a new approach to inverse reinforcement learning (IRL) based on the deep Gaussian process (deep GP) model, which is capable of learning complicated reward structures with few demonstrations. Our model stacks multiple latent GP layers to learn abstract representations of the state feature space, which is linked to the demonstrations through the Maximum Entropy learning framework. Incorporating the IRL engine into the nonlinear latent structure renders existing deep GP inference approaches intractable. To tackle this, we develop a non-standard variational approximation framework which extends previous inference schemes. This allows for approximate Bayesian treatment of the feature space and guards against overfitting. Carrying out representation and inverse reinforcement learning simultaneously within our model outperforms state-of-the-art approaches, as we demonstrate with experiments on standard benchmarks ("object world","highway driving") and a new benchmark ("binary world").

How to make machines learn on their own similar to humans? This is the pivotal question for the development of artificial intelligence. To develop intelligent machines and systems (artificial intelligence), we need to understand how human intelligence and learning work. For this, we will explore the ideas behind reinforcement learning. In the process, we will also explore answers to these seemingly unrelated questions.

An AI has learned to tackle one of the toughest Atari video games by taking instructions in plain English. The system, developed by a team at Stanford University in California, learned to play the game Montezuma's Revenge, in which players scour an Aztec temple for treasure. The game is challenging for AI to learn because it offers sparse rewards, requiring players to make several moves before earning any points. Most video-game-playing AIs use reinforcement learning to develop a strategy, relying on feedback like game points to tell them when they are playing well. To help their AI pick up game tactics quicker, the Stanford team gave their reinforcement learning system a helping hand in the form of natural language instructions, for example advising it to "climb up the ladder" or "get the key".

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself. Consequently, we must search the space of programs for those that output the correct result, while not being misled by spurious programs: incorrect programs that coincidentally output the correct result. We connect two common learning paradigms, reinforcement learning (RL) and maximum marginal likelihood (MML), and then present a new learning algorithm that combines the strengths of both. The new algorithm guards against spurious programs by combining the systematic search traditionally employed in MML with the randomized exploration of RL, and by updating parameters such that probability is spread more evenly across consistent programs. We apply our learning algorithm to a new neural semantic parser and show significant gains over existing state-of-the-art results on a recent context-dependent semantic parsing task.

An AI has learned to tackle one of the toughest Atari videogames by taking instructions in plain English. The system, developed by a team at Stanford University in California, learned to play the game Montezuma's Revenge, in which players scour an Aztec temple for treasure. The game is challenging for AI to learn because it offers sparse rewards, requiring players to make several moves before earning any points. Most videogame-playing AIs use reinforcement learning to develop a strategy, relying on feedback like game points to tell them when they are playing well. To help their AI pick up game tactics quicker, the Stanford team gave their reinforcement learning system a helping hand in the form of natural language instructions, for example advising it to "climb up the ladder" or "get the key".

In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a new model, the Semi Aggregated Markov Decision Process (SAMDP), and an algorithm that learns it automatically. The SAMDP model allows us to identify spatio-temporal abstractions directly from features and may be used as a sub-goal detector in future work. Using our tools we reveal that the features learned by DQNs aggregate the state space in a hierarchical fashion, explaining its success. Moreover, we are able to understand and describe the policies learned by DQNs for three different Atari2600 games and suggest ways to interpret, debug and optimize deep neural networks in reinforcement learning.

Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs).

Temporal difference learning and Residual Gradient methods are the most widely used temporal difference based learning algorithms; however, it has been shown that none of their objective functions is optimal w.r.t approximating the true value function $V$. Two novel algorithms are proposed to approximate the true value function $V$. This paper makes the following contributions: (1) A batch algorithm that can help find the approximate optimal off-policy prediction of the true value function $V$. (2) A linear computational cost (per step) near-optimal algorithm that can learn from a collection of off-policy samples. (3) A new perspective of the emphatic temporal difference learning which bridges the gap between off-policy optimality and off-policy stability.

Recently, I was fortunate enough to be awarded a Data61 summer research scholarship from the CSIRO. This post is the second of a 3 part series detailing what I learned, the conclusions I came to and some mistakes I made along the way. My chosen topic was Deep Q-Learning For Self-Driving Cars. This installment outlines my implementation of Deep Q-Learning to navigate a straight stretch of simulated highway. The end goal of the project is to train a model well enough to control an RC Car, then, if all goes well, something larger.