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Machine Learning Street Talk (MLST)
#114 - Secrets of Deep Reinforcement Learning (Minqi Jiang)
Apr 16, 2023
02:47:15
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Quick takeaways
- Deep RL balances serendipity and planning for success in startups.
- Understanding robustness in RL and differences from supervised learning is crucial.
- Exploring intelligence in environments highlights emergence and open-endedness in AI.
- Language models may plateau without novel data, sparking debates on creativity.
- Transition to Software 2.0 involves active learning for robust and accurate models.
- Open-ended learning methods prevent model stagnation and enhance adaptability in continual learning.
Deep dives
Language Model Training and Human Innovation
Language models, like the discussed ones, are crucial agents in co-evolving civilizations with humans, accelerating cultural innovations. While language models might run out of novel training data in the future, their advancement is closely tied to the pace of human-generated data and civilization's growth. As long as humans continue to innovate and create novelty, language models will serve as an enhanced tool in fueling further cultural and intellectual advancements.
Evolutionary and Open-Ended Intelligence
The essence of intelligence can be traced back to the process of evolution itself, which embodies meta-learning and intelligent problem-solving dynamics. Language models, while displaying remarkable mimicry and performance, are products of the human intelligence embedded in static repositories of knowledge. The true intelligence is vested in the process that created them, showcasing the underlying intelligence of evolutionary mechanisms.
Asymptotic Intelligence Expansion
Despite assertions of a potential intelligence explosion with advanced AI models or superintelligence, the concept of asymptotes in intelligence development remains prevalent. The notion that even with highly sophisticated systems, there are bounds to exponential growth and open-ended problem-solving abilities suggests that there will be limits to the trajectory of intelligent systems, possibly converging at certain points.
Self-Improvement and Recursive Training in Language Models
Exploring the recursive nature of language model training, especially when they might rely on their own outputs for further learning, raises questions about the possibility of self-improvement loops within the models. Self-instruct methodologies and innovations in training strategies may enable language models to advance through self-generated data, leading to more intricate exploration and training dynamics.
Philosophical Considerations on Innovation and Human-AI Interaction
The philosophical discourse surrounding the interaction of humans and AI, particularly in terms of innovation, touches on the depth of the relationship between human creativity and technological advancements. The intertwining of intelligent systems with human innovation propels the evolution of cultures and societies, highlighting a shared journey in stimulating intellectual progress and cultural ingenuity.
Using Synthetic Tasks to Improve Language Models
Training language models on self-generated synthetic tasks can lead to improvements in performance on various benchmarks. The Toolformer paper introduces a concept where the language model prompts access to an external API, allowing for the filtering of successful instances to enhance model training. This approach enables the model to teach itself how to leverage tools and APIs for performance improvement.
Challenges with Language Models and Open-Ended Learning
In an infinite time with infinite compute, language models may reach a plateau due to being trained on fixed datasets, limiting their ability to generate data beyond the dataset. The debate on whether language models can create new information or merely reproduce existing data raises questions about the models' capacity for open-endedness.
Software 2.0 and Machine Teaching
Transitioning to Software 2.0 involves moving from rule-based AI systems to auto-programming models like neural networks. Machine teaching methods such as Pickle from Microsoft Research enable human supervisors to interactively train ML models by selecting the most relevant training data, leading to robust and accurate results with minimal data. The concept of active learning focuses on knowing which data to train on to continuously improve the model.
Avoiding Convergence and Identifying Stagnation in Learning
To prevent models from converging to suboptimal solutions, open-ended learning methods aim to introduce divergent processes that prevent equilibrium. Recognizing stagnation in learning involves monitoring the changes in model weights to identify when the model has stopped evolving, indicating the need to adapt the environment to encourage continual learning.
Key Point 1: Challenges of Neural Plasticity in Continual Learning
Neural plasticity poses challenges in continual learning environments, where networks can stop learning due to mechanistic issues like dead neurons affecting weight updates. Automatic curriculum learning methods enhance agent robustness by generating new tasks leading to continual learning on diverse tasks, but issues such as loss of plasticity and neuron zeroing out can impact network improvements.
Key Point 2: Non-Stationary Atari Benchmarks and Neuron Adaptation
Researchers introduced a non-stationary version of Atari benchmarks to test agent adaptability through rotations of gameplay cycles. Discoveries indicate that neural plasticity limitations, including dead neuron problems in networks using relu activations, hinder performance improvement. By replacing relu activations with krelu, utilizing concatenation of relu outputs to ensure continuous gradients and network updates, performance challenges related to plasticity were addressed effectively.
Key Point 3: Unsupervised Environment Design and Curriculum Learning
Unsupervised environment design (UED) offers a structured approach in curriculum learning for training agents within diverse environments. UED methods can be applied effectively to domains like robotics and simulations where enhancing agent robustness and adaptability across varied tasks is crucial. By leveraging generative models and contextual MDP extensions, UED facilitates goal-directed learning and adaptable exploration strategies for optimizing reinforcement learning policies.
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In this exclusive interview, Dr. Tim Scarfe sits down with Minqi Jiang, a leading PhD student at University College London and Meta AI, as they delve into the fascinating world of deep reinforcement learning (RL) and its impact on technology, startups, and research. Discover how Minqi made the crucial decision to pursue a PhD in this exciting field, and learn from his valuable startup experiences and lessons.
Minqi shares his insights into balancing serendipity and planning in life and research, and explains the role of objectives and Goodhart's Law in decision-making. Get ready to explore the depths of robustness in RL, two-player zero-sum games, and the differences between RL and supervised learning.
As they discuss the role of environment in intelligence, emergence, and abstraction, prepare to be blown away by the possibilities of open-endedness and the intelligence explosion. Learn how language models generate their own training data, the limitations of RL, and the future of software 2.0 with interpretability concerns.
From robotics and open-ended learning applications to learning potential metrics and MDPs, this interview is a goldmine of information for anyone interested in AI, RL, and the cutting edge of technology. Don't miss out on this incredible opportunity to learn from a rising star in the AI world!
TOC
Tech & Startup Background [00:00:00]
Pursuing PhD in Deep RL [00:03:59]
Startup Lessons [00:11:33]
Serendipity vs Planning [00:12:30]
Objectives & Decision Making [00:19:19]
Minimax Regret & Uncertainty [00:22:57]
Robustness in RL & Zero-Sum Games [00:26:14]
RL vs Supervised Learning [00:34:04]
Exploration & Intelligence [00:41:27]
Environment, Emergence, Abstraction [00:46:31]
Open-endedness & Intelligence Explosion [00:54:28]
Language Models & Training Data [01:04:59]
RLHF & Language Models [01:16:37]
Creativity in Language Models [01:27:25]
Limitations of RL [01:40:58]
Software 2.0 & Interpretability [01:45:11]
Language Models & Code Reliability [01:48:23]
Robust Prioritized Level Replay [01:51:42]
Open-ended Learning [01:55:57]
Auto-curriculum & Deep RL [02:08:48]
Robotics & Open-ended Learning [02:31:05]
Learning Potential & MDPs [02:36:20]
Universal Function Space [02:42:02]
Goal-Directed Learning & Auto-Curricula [02:42:48]
Advice & Closing Thoughts [02:44:47]
References:
- Why Greatness Cannot Be Planned: The Myth of the Objective by Kenneth O. Stanley and Joel Lehman
https://www.springer.com/gp/book/9783319155234
- Rethinking Exploration: General Intelligence Requires Rethinking Exploration
https://arxiv.org/abs/2106.06860
- The Case for Strong Emergence (Sabine Hossenfelder)
https://arxiv.org/abs/2102.07740
- The Game of Life (Conway)
https://www.conwaylife.com/
- Toolformer: Teaching Language Models to Generate APIs (Meta AI)
https://arxiv.org/abs/2302.04761
- OpenAI's POET: Paired Open-Ended Trailblazer
https://arxiv.org/abs/1901.01753
- Schmidhuber's Artificial Curiosity
https://people.idsia.ch/~juergen/interest.html
- Gödel Machines
https://people.idsia.ch/~juergen/goedelmachine.html
- PowerPlay
https://arxiv.org/abs/1112.5309
- Robust Prioritized Level Replay: https://openreview.net/forum?id=NfZ6g2OmXEk
- Unsupervised Environment Design: https://arxiv.org/abs/2012.02096
- Excel: Evolving Curriculum Learning for Deep Reinforcement Learning
https://arxiv.org/abs/1901.05431
- Go-Explore: A New Approach for Hard-Exploration Problems
https://arxiv.org/abs/1901.10995
- Learning with AMIGo: Adversarially Motivated Intrinsic Goals
https://www.researchgate.net/publication/342377312_Learning_with_AMIGo_Adversarially_Motivated_Intrinsic_Goals
PRML
https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
Sutton and Barto
https://web.stanford.edu/class/psych209/Readings/SuttonBartoIPRLBook2ndEd.pdf
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