We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a trained environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several strong baselines.

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a trained environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several strong baselines.

This paper presents an approach to model-based reinforcement learning where, instead of directly estimating the value of actions in a learned model, a neural network processes the model's predictions, combining with model-free features, to produce a policy and/or value function. The idea is that since the model is likely to be flawed, the network may be able to extract useful information from the model's predictions while ignoring unreliable information. The approach is studied in procedurally generated Sokoban puzzles and a synthetic Pac-Man-like environment and is shown to outperform purely model-free learning as well as MCTS on the learned model. The experiments are thorough and carefully designed to tease issues apart and to clearly answer well-stated questions about the approach. I found the experiments to provide convincing evidence that I2A is taking advantage of the learned model, is robust to model flaws, and can leverage the learned model for multiple tasks.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Creating agents that resemble the cognitive abilities of the human brain has been one of the most elusive goals of the artificial intelligence(AI) space. Recently, I've been spending time on a couple of scenarios that relate to imagination in deep learning systems which reminded me of a very influential paper Alphabet's subsidiary DeepMind published last year in this subject.

I recently started a new newsletter focus on AI education. TheSequence is a no-BS (meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Creating agents that resemble the cognitive abilities of the human brain has been one of the most elusive goals of the artificial intelligence(AI) space. Recently, I've been spending time on a couple of scenarios that relate to imagination in deep learning systems which reminded me of a very influential paper Alphabet's subsidiary DeepMind published last year in this subject.

We introduce Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects. In contrast to most existing model-based reinforcement learning and planning methods, which prescribe how a model should be used to arrive at a policy, I2As learn to interpret predictions from a trained environment model to construct implicit plans in arbitrary ways, by using the predictions as additional context in deep policy networks. I2As show improved data efficiency, performance, and robustness to model misspecification compared to several strong baselines. Papers published at the Neural Information Processing Systems Conference.

Creating agents that resemble the cognitive abilities of the human brain has been one of the most elusive goals of the artificial intelligence(AI) space. Recently, I've been spending time on a couple of scenarios that relate to imagination in deep learning systems which reminded me of a very influential paper Alphabet's subsidiary DeepMind published last year in this subject. Imagination is one of those magical features of the human mind that differentiate us from other species. From the neuroscience standpoint, imagination is the ability of the brain to form images or sensations without any immediate sensorial input. Imagination is a key element of our learning process as it enable us to apply knowledge to specific problems and better plan for future outcomes.

Google-owned DeepMind is working on artificial intelligence (AI) that can imagine like humans and handle the unpredictable scenarios in real world. According to a report in Wired on Thursday, DeepMind, that was acquired by Google in 2014, is developing an AI capable of'imagination', enabling machines to see the consequences of their actions before they make them. "Its attempt to create algorithms that simulate the distinctly human ability to construct a plan could eventually help to produce software and hardware capable of solving complex tasks more efficiently," the report noted. DeepMind was successful in AI when it developed'AlphaGo' that recently beat a series of human champions at the tricky board game'Go'. But in case of'AlphaGo', there are a set of defined rules and predictable outcomes.

Google's DeepMind is developing an AI capable of'imagination', enabling machines to see the consequences of their actions before they make them. In two new research papers, the British AI firm, which was acquired by Google in 2014, describes new developments for "imagination-based planning" to AI. Its attempt to create algorithms that simulate the distinctly human ability to construct a plan could eventually help to produce software and hardware capable of solving complex tasks more efficiently. DeepMind's previous research in this area has been incredibly successful, with its AlphaGo AI managing to beat a series of human champions at the notoriously tricky board game Go. However, AlphaGo relies on a clearly defined set of rules to provide likely outcomes, with relatively few factors to consider. "The real world is complex, rules are not so clearly defined and unpredictable problems often arise," explain the DeepMind researchers in a blog post.