🎯 Top Personalized Recommendations
ISCTE University
AI Summary
- The KL divergence measures the difference between two probability distributions. [3]
- The chain rule of calculus is used to find the derivative of a composite function. [3]
- Kullback-Leibler (KL) Divergence: A measure of the difference between two probability distributions. [3]
- Chain Rule of Calculus: A method for finding the derivative of a composite function. [3]
- The KL divergence is used to compare and evaluate the similarity between two probability distributions. [3]
- The chain rule of calculus is essential in understanding how changes in one variable affect another variable in a complex system. [3]
- Lack of clear examples to illustrate the application of KL divergence and chain rule of calculus. [3]
- The KL divergence is widely used in information theory, statistics, and machine learning to compare probability distributions. [3]
- The chain rule of calculus has numerous applications in physics, engineering, and economics to model complex systems and optimize functions. [3]
- Insufficient explanation of the mathematical derivations behind the formulas. [1]
Abstract
Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.
Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.
Why we think this paper is great for you:
This paper directly addresses the intersection of deep learning and reinforcement learning, a core area of interest for the user. It provides a foundational understanding of learning-based approaches to complex sequential decision-making problems.
This paper directly addresses the intersection of deep learning and reinforcement learning, a core area of interest for the user. It provides a foundational understanding of learning-based approaches to complex sequential decision-making problems.
Australian National Unv
AI Summary
- TD( /AL) updates can lead to inferior policies STD( /AL) retains the advantages of TD( /AL) in terms of on-line operation and small memory requirements STD( /AL) operates directly on the difference in values between sibling states, rather than the state values themselves Temporal Difference Learning (TD( /AL)) is a type of reinforcement learning algorithm that uses the difference between the estimated value of a state and its predicted value to update the policy. [3]
- S TD( /AL) stands for Stable Temporal Difference Learning, which is an extension of TD( /AL) that adds a stability term to prevent divergence. [3]
- The limiting behavior of STD( /AL) was characterized for linear function approximators, yielding an interpretation that S TD( /AL) acts to improve policies rather than the state values themselves. [3]
- The paper assumes a linear function approximator and does not consider non-linear function approximators. [3]
- Binary Markov Decision Processes (BMDPs) are a type of decision-making problem where the state space is finite and the actions are binary. [2]
- The paper presents a new algorithm, STD( /AL), which retains the advantages of TD( /AL) in terms of on-line operation and small memory requirements but operates directly on the difference in values between sibling states. [1]
Abstract
TD($λ$) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD($λ$) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD($λ$)--starting from an optimal policy--converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD($λ$), called STD($λ$), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD($λ$) in the context of the two-state system, is presented, along with a comparison with Bertsekas' differential training method [1]. This is followed by successful demonstrations of STD($λ$) on the two-state system and a variation on the well known acrobot problem.
TD($λ$) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD($λ$) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD($λ$)--starting from an optimal policy--converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD($λ$), called STD($λ$), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD($λ$) in the context of the two-state system, is presented, along with a comparison with Bertsekas' differential training method [1]. This is followed by successful demonstrations of STD($λ$) on the two-state system and a variation on the well known acrobot problem.
Why we think this paper is great for you:
This paper explores a fundamental algorithm in reinforcement learning, offering insights into how agents can learn optimal policies through temporal difference learning. Understanding this algorithm is crucial for developing sophisticated RL systems.
This paper explores a fundamental algorithm in reinforcement learning, offering insights into how agents can learn optimal policies through temporal difference learning. Understanding this algorithm is crucial for developing sophisticated RL systems.
ETH Zrich
AI Summary
- The tabletop circulating water channel (CWC) is used for active flow control experiments. [3]
- The CWC consists of an upper and lower branch, with the test section housing a cylinder in the upper branch. [3]
- Three brushless propellers drive a left-to-right flow, which is redirected into the lower branch by guide vanes and then back to the upper branch. [3]
- A honeycomb structure straightens the flow and suppresses large-scale vortices, while flow restrictions accelerate the stream and stretch remaining small-scale vortices. [3]
- The particle image velocimetry (PIV) setup captures images at 60 Hz to estimate the flow field in real time. [1]
Abstract
Many high-performance human activities are executed with little or no external feedback: think of a figure skater landing a triple jump, a pitcher throwing a curveball for a strike, or a barista pouring latte art. To study the process of skill acquisition under fully controlled conditions, we bypass human subjects. Instead, we directly interface a generalist reinforcement learning agent with a spinning cylinder in a tabletop circulating water channel to maximize or minimize drag. This setup has several desirable properties. First, it is a physical system, with the rich interactions and complex dynamics that only the physical world has: the flow is highly chaotic and extremely difficult, if not impossible, to model or simulate accurately. Second, the objective -- drag minimization or maximization -- is easy to state and can be captured directly in the reward, yet good strategies are not obvious beforehand. Third, decades-old experimental studies provide recipes for simple, high-performance open-loop policies. Finally, the setup is inexpensive and far easier to reproduce than human studies. In our experiments we find that high-dimensional flow feedback lets the agent discover high-performance drag-control strategies with only minutes of real-world interaction. When we later replay the same action sequences without any feedback, we obtain almost identical performance. This shows that feedback, and in particular flow feedback, is not needed to execute the learned policy. Surprisingly, without flow feedback during training the agent fails to discover any well-performing policy in drag maximization, but still succeeds in drag minimization, albeit more slowly and less reliably. Our studies show that learning a high-performance skill can require richer information than executing it, and learning conditions can be kind or wicked depending solely on the goal, not on dynamics or policy complexity.
Many high-performance human activities are executed with little or no external feedback: think of a figure skater landing a triple jump, a pitcher throwing a curveball for a strike, or a barista pouring latte art. To study the process of skill acquisition under fully controlled conditions, we bypass human subjects. Instead, we directly interface a generalist reinforcement learning agent with a spinning cylinder in a tabletop circulating water channel to maximize or minimize drag. This setup has several desirable properties. First, it is a physical system, with the rich interactions and complex dynamics that only the physical world has: the flow is highly chaotic and extremely difficult, if not impossible, to model or simulate accurately. Second, the objective -- drag minimization or maximization -- is easy to state and can be captured directly in the reward, yet good strategies are not obvious beforehand. Third, decades-old experimental studies provide recipes for simple, high-performance open-loop policies. Finally, the setup is inexpensive and far easier to reproduce than human studies. In our experiments we find that high-dimensional flow feedback lets the agent discover high-performance drag-control strategies with only minutes of real-world interaction. When we later replay the same action sequences without any feedback, we obtain almost identical performance. This shows that feedback, and in particular flow feedback, is not needed to execute the learned policy. Surprisingly, without flow feedback during training the agent fails to discover any well-performing policy in drag maximization, but still succeeds in drag minimization, albeit more slowly and less reliably. Our studies show that learning a high-performance skill can require richer information than executing it, and learning conditions can be kind or wicked depending solely on the goal, not on dynamics or policy complexity.
Why we think this paper is great for you:
The paper investigates the impact of feedback on skill acquisition, a key question in understanding how agents learn complex behaviors. This aligns with the user's interest in the mechanisms underlying skill development.
The paper investigates the impact of feedback on skill acquisition, a key question in understanding how agents learn complex behaviors. This aligns with the user's interest in the mechanisms underlying skill development.
The University of Hongk
AI Summary
- DeepCode's performance is significantly better than the best LLM agent baseline, with a 70% relative improvement. [3]
- LLM: Large Language Model BasicAgent: A general-purpose agent scaffolding IterativeAgent: An improved version of BasicAgent DeepCode outperforms all other baselines, including human experts and state-of-the-art commercial code agents. [3]
- The paper does not provide a detailed explanation of the algorithm used in DeepCode. [3]
- The paper presents a framework called DeepCode that is designed to transform machine learning papers into executable code. [3]
- It uses systematic planning, structured code generation, and automated verification to achieve high performance. [3]
- Imagine you have a machine learning paper that describes how to build a new AI model. [2]
- Previous work on code generation has focused on general-purpose agents, but DeepCode's specialized design provides significant advantages over these approaches. [1]
Abstract
Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery.
Recent advances in large language models (LLMs) have given rise to powerful coding agents, making it possible for code assistants to evolve into code engineers. However, existing methods still face significant challenges in achieving high-fidelity document-to-codebase synthesis--such as scientific papers to code--primarily due to a fundamental conflict between information overload and the context bottlenecks of LLMs. In this work, we introduce DeepCode, a fully autonomous framework that fundamentally addresses this challenge through principled information-flow management. By treating repository synthesis as a channel optimization problem, DeepCode seamlessly orchestrates four information operations to maximize task-relevant signals under finite context budgets: source compression via blueprint distillation, structured indexing using stateful code memory, conditional knowledge injection via retrieval-augmented generation, and closed-loop error correction. Extensive evaluations on the PaperBench benchmark demonstrate that DeepCode achieves state-of-the-art performance, decisively outperforming leading commercial agents such as Cursor and Claude Code, and crucially, surpassing PhD-level human experts from top institutes on key reproduction metrics. By systematically transforming paper specifications into production-grade implementations comparable to human expert quality, this work establishes new foundations for autonomous scientific reproduction that can accelerate research evaluation and discovery.
Why we think this paper is great for you:
This paper examines the use of large language models for coding agents, a rapidly developing area within agentic reinforcement learning. It's relevant to the user's interest in intelligent agents capable of complex tasks.
This paper examines the use of large language models for coding agents, a rapidly developing area within agentic reinforcement learning. It's relevant to the user's interest in intelligent agents capable of complex tasks.
University College London
AI Summary
- The thesis explores the concept of open-endedness in AI systems and its potential to overcome limitations imposed by static datasets and fixed learning environments. [3]
- Open-ended systems continuously generate novel challenges, allowing for continuous improvement and broader generalisation to unseen tasks. [3]
- Open-ended system: A system that continuously generates novel challenges, allowing for continuous improvement and broader generalisation to unseen tasks. [3]
- AI systems often excel only at tasks similar to their training data, revealing critical limitations when faced with tasks that differ significantly. [3]
- Procedural content generation (PCG) is used as a framework for testing the robustness and generality of RL methods in complex environments. [2]
Abstract
The growing prevalence of artificial intelligence (AI) in various applications underscores the need for agents that can successfully navigate and adapt to an ever-changing, open-ended world. A key challenge is ensuring these AI agents are robust, excelling not only in familiar settings observed during training but also effectively generalising to previously unseen and varied scenarios. In this thesis, we harness methodologies from open-endedness and multi-agent learning to train and evaluate robust AI agents capable of generalising to novel environments, out-of-distribution inputs, and interactions with other co-player agents. We begin by introducing MiniHack, a sandbox framework for creating diverse environments through procedural content generation. Based on the game of NetHack, MiniHack enables the construction of new tasks for reinforcement learning (RL) agents with a focus on generalisation. We then present Maestro, a novel approach for generating adversarial curricula that progressively enhance the robustness and generality of RL agents in two-player zero-sum games. We further probe robustness in multi-agent domains, utilising quality-diversity methods to systematically identify vulnerabilities in state-of-the-art, pre-trained RL policies within the complex video game football domain, characterised by intertwined cooperative and competitive dynamics. Finally, we extend our exploration of robustness to the domain of LLMs. Here, our focus is on diagnosing and enhancing the robustness of LLMs against adversarial prompts, employing evolutionary search to generate a diverse range of effective inputs that aim to elicit undesirable outputs from an LLM. This work collectively paves the way for future advancements in AI robustness, enabling the development of agents that not only adapt to an ever-evolving world but also thrive in the face of unforeseen challenges and interactions.
The growing prevalence of artificial intelligence (AI) in various applications underscores the need for agents that can successfully navigate and adapt to an ever-changing, open-ended world. A key challenge is ensuring these AI agents are robust, excelling not only in familiar settings observed during training but also effectively generalising to previously unseen and varied scenarios. In this thesis, we harness methodologies from open-endedness and multi-agent learning to train and evaluate robust AI agents capable of generalising to novel environments, out-of-distribution inputs, and interactions with other co-player agents. We begin by introducing MiniHack, a sandbox framework for creating diverse environments through procedural content generation. Based on the game of NetHack, MiniHack enables the construction of new tasks for reinforcement learning (RL) agents with a focus on generalisation. We then present Maestro, a novel approach for generating adversarial curricula that progressively enhance the robustness and generality of RL agents in two-player zero-sum games. We further probe robustness in multi-agent domains, utilising quality-diversity methods to systematically identify vulnerabilities in state-of-the-art, pre-trained RL policies within the complex video game football domain, characterised by intertwined cooperative and competitive dynamics. Finally, we extend our exploration of robustness to the domain of LLMs. Here, our focus is on diagnosing and enhancing the robustness of LLMs against adversarial prompts, employing evolutionary search to generate a diverse range of effective inputs that aim to elicit undesirable outputs from an LLM. This work collectively paves the way for future advancements in AI robustness, enabling the development of agents that not only adapt to an ever-evolving world but also thrive in the face of unforeseen challenges and interactions.
Why we think this paper is great for you:
The paper focuses on building robust agents capable of adapting to dynamic environments, a critical aspect of agentic reinforcement learning. This directly addresses the challenge of creating agents that can thrive in open-ended scenarios.
The paper focuses on building robust agents capable of adapting to dynamic environments, a critical aspect of agentic reinforcement learning. This directly addresses the challenge of creating agents that can thrive in open-ended scenarios.
ISCTE University
AI Summary
- The KL divergence measures the difference between two probability distributions. [3]
- The chain rule of calculus is used to find the derivative of a composite function. [3]
- Kullback-Leibler (KL) Divergence: A measure of the difference between two probability distributions. [3]
- Chain Rule of Calculus: A method for finding the derivative of a composite function. [3]
- The KL divergence is used to compare and evaluate the similarity between two probability distributions. [3]
- The chain rule of calculus is essential in understanding how changes in one variable affect another variable in a complex system. [3]
- Lack of clear examples to illustrate the application of KL divergence and chain rule of calculus. [3]
- The KL divergence is widely used in information theory, statistics, and machine learning to compare probability distributions. [3]
- The chain rule of calculus has numerous applications in physics, engineering, and economics to model complex systems and optimize functions. [3]
- Insufficient explanation of the mathematical derivations behind the formulas. [1]
Abstract
Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.
Embodied agents, such as robots and virtual characters, must continuously select actions to execute tasks effectively, solving complex sequential decision-making problems. Given the difficulty of designing such controllers manually, learning-based approaches have emerged as promising alternatives, most notably Deep Reinforcement Learning (DRL) and Deep Imitation Learning (DIL). DRL leverages reward signals to optimize behavior, while DIL uses expert demonstrations to guide learning. This document introduces DRL and DIL in the context of embodied agents, adopting a concise, depth-first approach to the literature. It is self-contained, presenting all necessary mathematical and machine learning concepts as they are needed. It is not intended as a survey of the field; rather, it focuses on a small set of foundational algorithms and techniques, prioritizing in-depth understanding over broad coverage. The material ranges from Markov Decision Processes to REINFORCE and Proximal Policy Optimization (PPO) for DRL, and from Behavioral Cloning to Dataset Aggregation (DAgger) and Generative Adversarial Imitation Learning (GAIL) for DIL.
Why we think this paper is great for you:
This paper provides a foundational understanding of learning-based approaches to complex sequential decision-making problems, a core area of interest for the user. It provides a foundational understanding of learning-based approaches to complex sequential decision-making problems.
This paper provides a foundational understanding of learning-based approaches to complex sequential decision-making problems, a core area of interest for the user. It provides a foundational understanding of learning-based approaches to complex sequential decision-making problems.
Australian National Unv
AI Summary
- TD( /AL) updates can lead to inferior policies STD( /AL) retains the advantages of TD( /AL) in terms of on-line operation and small memory requirements STD( /AL) operates directly on the difference in values between sibling states, rather than the state values themselves Temporal Difference Learning (TD( /AL)) is a type of reinforcement learning algorithm that uses the difference between the estimated value of a state and its predicted value to update the policy. [3]
- S TD( /AL) stands for Stable Temporal Difference Learning, which is an extension of TD( /AL) that adds a stability term to prevent divergence. [3]
- The limiting behavior of STD( /AL) was characterized for linear function approximators, yielding an interpretation that S TD( /AL) acts to improve policies rather than the state values themselves. [3]
- The paper assumes a linear function approximator and does not consider non-linear function approximators. [3]
- Binary Markov Decision Processes (BMDPs) are a type of decision-making problem where the state space is finite and the actions are binary. [2]
- The paper presents a new algorithm, STD( /AL), which retains the advantages of TD( /AL) in terms of on-line operation and small memory requirements but operates directly on the difference in values between sibling states. [1]
Abstract
TD($λ$) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD($λ$) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD($λ$)--starting from an optimal policy--converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD($λ$), called STD($λ$), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD($λ$) in the context of the two-state system, is presented, along with a comparison with Bertsekas' differential training method [1]. This is followed by successful demonstrations of STD($λ$) on the two-state system and a variation on the well known acrobot problem.
TD($λ$) with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD($λ$) has been shown to minimise the squared error between the approximate value of each state and the true value. However, as far as policy is concerned, it is error in the relative ordering of states that is critical, rather than error in the state values. We illustrate this point, both in simple two-state and three-state systems in which TD($λ$)--starting from an optimal policy--converges to a sub-optimal policy, and also in backgammon. We then present a modified form of TD($λ$), called STD($λ$), in which function approximators are trained with respect to relative state values on binary decision problems. A theoretical analysis, including a proof of monotonic policy improvement for STD($λ$) in the context of the two-state system, is presented, along with a comparison with Bertsekas' differential training method [1]. This is followed by successful demonstrations of STD($λ$) on the two-state system and a variation on the well known acrobot problem.
Why we think this paper is great for you:
This paper explores a fundamental algorithm in reinforcement learning, offering insights into how agents can learn optimal policies through temporal difference learning. Understanding this algorithm is crucial for developing sophisticated RL systems.
This paper explores a fundamental algorithm in reinforcement learning, offering insights into how agents can learn optimal policies through temporal difference learning. Understanding this algorithm is crucial for developing sophisticated RL systems.
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