Learning, Agent, Reinforcement learning, Machine learning, Artificial intelligence, The Reward

Evaluating Agents without Rewards

On Dec 23, 2020
@Miles_Brundage shared
RT @danijarh: Excited to share Evaluating Agents without Rewards! We compare intrinsic objectives with task reward and similarity to human players. Turns out they all correlate more w/ human than w/ reward. Two of them even correlate more w/ human than reward does. https://t.co/hctzH7cWFz 👇 https://t.co/TmiDQcRUqL
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Objective RewardCorrelation Human Similarity Correlation Task Reward 1.00 0.67 Human Similarity 0.67 1.00 Input Entropy 0.54 0.89 Information Gain 0.49 0.79 Empowerment 0.41 0.66 Table 1: Pearson correlation coefficients between each in- trinsic objective and task reward or human ...

arxiv.org
On Dec 23, 2020
@Miles_Brundage shared
RT @danijarh: Excited to share Evaluating Agents without Rewards! We compare intrinsic objectives with task reward and similarity to human players. Turns out they all correlate more w/ human than w/ reward. Two of them even correlate more w/ human than reward does. https://t.co/hctzH7cWFz 👇 https://t.co/TmiDQcRUqL
Open

Evaluating Agents without Rewards

Evaluating Agents without Rewards

Objective RewardCorrelation Human Similarity Correlation Task Reward 1.00 0.67 Human Similarity 0.67 1.00 Input Entropy 0.54 0.89 Information Gain 0.49 0.79 Empowerment 0.41 0.66 Table 1: ...

Deepmind’s Gaming Streak: The Rise of AI Dominance

Deepmind’s Gaming Streak: The Rise of AI Dominance

There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.

Click here to read the article

Click here to read the article

We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning ...

What Are Major Reinforcement Learning Achievements & Papers From 2018?

What Are Major Reinforcement Learning Achievements & Papers From 2018?

Is reinforcement learning finally useful for business applications beyond just games and robotics? Recent advances in increased data efficiency and stability, multi-tasking, and the ...

Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

Creating a Zoo of Atari-Playing Agents to Catalyze the Understanding of Deep Reinforcement Learning

Uber AI Labs releases Atari Model Zoo, an open source repository of both trained Atari Learning Environment agents and tools to better understand them.

DeepMind’s Agent57 beats humans at 57 classic Atari games

DeepMind’s Agent57 beats humans at 57 classic Atari games

Researchers at DeepMind claim they've developed an algorithm -- Agent57 -- that far outperforms humans on 57 different Atari games.

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

We've discovered that evolution strategies (ES), an optimization technique that's been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on modern ...

Exploration Strategies in Deep Reinforcement Learning

Exploration Strategies in Deep Reinforcement Learning

Exploitation versus exploration is a critical topic in reinforcement learning. This post introduces several common approaches for better exploration in Deep RL.

Review of my 2017 Forecasts

Review of my 2017 Forecasts

TL;DR: I got a lot of stuff right and a lot of stuff wrong. My Brier score (a measure of forecasting skill) was .22 , which is slightly better than chance but not great. My forecasts were ...