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Found 16 papers, 9 papers with code
ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization
The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms.
PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem
To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
Progressively Efficient Learning
By equipping a learning agent with an abstract, dynamic language and an intrinsic motivation to learn with minimal communication effort, CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate progressively efficiently by exchanging increasingly more abstract intentions.
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
In this paper, we propose $\texttt{COPlanner}$, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic environment exploration.
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
Moreover, we show that the policy gradient of Long-term Benefit Rate can be analytically reduced to standard policy gradient.
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations
To tackle this challenge, we propose GRAD, a novel game-theoretic approach that treats the temporally-coupled robust RL problem as a partially observable two-player zero-sum game.
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning.
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle.
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions.
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning
Recent studies reveal that a well-trained deep reinforcement learning (RL) policy can be particularly vulnerable to adversarial perturbations on input observations.
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions.
Transfer RL across Observation Feature Spaces via Model-Based Regularization
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e. g. increased number of observable features).
Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RL
Existing works on adversarial RL either use heuristics-based methods that may not find the strongest adversary, or directly train an RL-based adversary by treating the agent as a part of the environment, which can find the optimal adversary but may become intractable in a large state space.
Cortical Features for Defense Against Adversarial Audio Attacks
We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio.
