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Found 9 papers, 2 papers with code
Responsive Safety in Reinforcement Learning
This intuition leads to our introduction of PID control for the Lagrange multiplier in constrained RL, which we cast as a dynamical system.
A Hazard Analysis Framework for Code Synthesis Large Language Models
Codex, a large language model (LLM) trained on a variety of codebases, exceeds the previous state of the art in its capacity to synthesize and generate code.
Responsive Safety in Reinforcement Learning by PID Lagrangian Methods
Lagrangian methods are widely used algorithms for constrained optimization problems, but their learning dynamics exhibit oscillations and overshoot which, when applied to safe reinforcement learning, leads to constraint-violating behavior during agent training.
Towards Characterizing Divergence in Deep Q-Learning
Deep Q-Learning (DQL), a family of temporal difference algorithms for control, employs three techniques collectively known as the `deadly triad' in reinforcement learning: bootstrapping, off-policy learning, and function approximation.
Variational Option Discovery Algorithms
We explore methods for option discovery based on variational inference and make two algorithmic contributions.
On First-Order Meta-Learning Algorithms
This paper considers meta-learning problems, where there is a distribution of tasks, and we would like to obtain an agent that performs well (i. e., learns quickly) when presented with a previously unseen task sampled from this distribution.
Constrained Policy Optimization
For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function.
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
Exploration in complex domains is a key challenge in reinforcement learning, especially for tasks with very sparse rewards.
Easy Monotonic Policy Iteration
A key problem in reinforcement learning for control with general function approximators (such as deep neural networks and other nonlinear functions) is that, for many algorithms employed in practice, updates to the policy or $Q$-function may fail to improve performance---or worse, actually cause the policy performance to degrade.
