Neuroscience and Reinforcement Learning
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
#neuroscience #reinforcement_learning
https://www.princeton.edu/~yael/ICMLTutorial.pdf
Deep Learning and Computational Neuroscience
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
#neuroscience #reinforcement_learning
https://link.springer.com/article/10.1007/s12021-018-9360-6
Neuroinformatics
Deep Learning and Computational Neuroscience
Neuroinformatics -
Solving Rubik’s Cube with a Robot Hand
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
This is fascinating, make sure you read it.
Summary: OpenAI team trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn’t just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity.
https://openai.com/blog/solving-rubiks-cube/
#reinforcement_learning #machine_learning #robotics
Openai
Solving Rubik’s Cube with a robot hand
We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic…
Proximal Policy Optimization
Paper:
https://openai.com/blog/openai-baselines-ppo/
YouTube Video:
https://www.youtube.com/watch?v=5P7I-xPq8u8&list=PLLO4N3-FoY3feUsA3_XZvn5sXy9Ms8ayE&index=2
#reinforcement_learning #optimization
Paper:
https://openai.com/blog/openai-baselines-ppo/
YouTube Video:
https://www.youtube.com/watch?v=5P7I-xPq8u8&list=PLLO4N3-FoY3feUsA3_XZvn5sXy9Ms8ayE&index=2
#reinforcement_learning #optimization
Openai
Proximal Policy Optimization
We’re releasing a new class of reinforcement learning algorithms, Proximal Policy Optimization (PPO), which perform comparably or better than state-of-the-art approaches while being much simpler to implement and tune. PPO has become the default reinforcement…
PyTorch tutorial of various RL algorithms:
actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
https://github.com/higgsfield/RL-Adventure-2
#reinforcement_learning #pytorch
actor critic / proximal policy optimization / acer / ddpg / twin dueling ddpg / soft actor critic / generative adversarial imitation learning / hindsight experience replay
https://github.com/higgsfield/RL-Adventure-2
#reinforcement_learning #pytorch
GitHub
GitHub - higgsfield-ai/higgsfield: Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed…
Fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters - higgsfield-ai/higgsfield
Distill and Transfer Learning for Robust Multitask Reinforcement Learning
"Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (DIStill & TRAnsfer Learning). Instead of sharing parameters between the different workers, we propose to share a distilled policy that captures common behavior across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning."
https://www.youtube.com/watch?v=scf7Przmh7c
#reinforcement_learning #multi_task_learning #transfer_learning
"Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where efficiency may be improved through transfer across related tasks. In practice, however, this is not usually observed, because gradients from different tasks can interfere negatively, making learning unstable and sometimes even less data efficient. Another issue is the different reward schemes between tasks, which can easily lead to one task dominating the learning of a shared model. We propose a new approach for joint training of multiple tasks, which we refer to as Distral (DIStill & TRAnsfer Learning). Instead of sharing parameters between the different workers, we propose to share a distilled policy that captures common behavior across tasks. Each worker is trained to solve its own task while constrained to stay close to the shared policy, while the shared policy is trained by distillation to be the centroid of all task policies. Both aspects of the learning process are derived by optimizing a joint objective function. We show that our approach supports efficient transfer on complex 3D environments, outperforming several related methods. Moreover, the proposed learning process is more robust and more stable---attributes that are critical in deep reinforcement learning."
https://www.youtube.com/watch?v=scf7Przmh7c
#reinforcement_learning #multi_task_learning #transfer_learning
YouTube
Distill and transfer learning for robust multitask RL
Most deep reinforcement learning algorithms are data inefficient in complex and rich environments, limiting their applicability to many scenarios. One direction for improving data efficiency is multitask learning with shared neural network parameters, where…
A fruitful relationship between neuroscience and AI
https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI
#reinforcement_learning #machine_learning #neuroscience #artificial_intelligence
https://deepmind.com/blog/article/Dopamine-and-temporal-difference-learning-A-fruitful-relationship-between-neuroscience-and-AI
#reinforcement_learning #machine_learning #neuroscience #artificial_intelligence
Google DeepMind
Dopamine and temporal difference learning: A fruitful relationship between neuroscience and AI
Learning and motivation are driven by internal and external rewards. Many of our day-to-day behaviours are guided by predicting, or anticipating, whether a given action will result in a positive...
Book: The SOAR Cognitive Architecture
Introduction: in development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
https://dl.acm.org/doi/book/10.5555/2222503
#cognitive_science #neuroscience #reinforcement_learning #artificial_intelligence
Introduction: in development for thirty years, Soar is a general cognitive architecture that integrates knowledge-intensive reasoning, reactive execution, hierarchical reasoning, planning, and learning from experience, with the goal of creating a general computational system that has the same cognitive abilities as humans. In contrast, most AI systems are designed to solve only one type of problem, such as playing chess, searching the Internet, or scheduling aircraft departures. Soar is both a software system for agent development and a theory of what computational structures are necessary to support human-level agents. Over the years, both software system and theory have evolved. This book offers the definitive presentation of Soar from theoretical and practical perspectives, providing comprehensive descriptions of fundamental aspects and new components. The current version of Soar features major extensions, adding reinforcement learning, semantic memory, episodic memory, mental imagery, and an appraisal-based model of emotion. This book describes details of Soar's component memories and processes and offers demonstrations of individual components, components working in combination, and real-world applications. Beyond these functional considerations, the book also proposes requirements for general cognitive architectures and explicitly evaluates how well Soar meets those requirements.
https://dl.acm.org/doi/book/10.5555/2222503
#cognitive_science #neuroscience #reinforcement_learning #artificial_intelligence
Reinforcement Learning and Optimal Control.pdf
2.7 MB
Reinforcement learning and Optimal Control (Draft version)
Desperately looking for the original version of this book. If you could find it, please let me know.
#reinforcement_learning #optimal_control
Desperately looking for the original version of this book. If you could find it, please let me know.
#reinforcement_learning #optimal_control