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Found 16 papers, 11 papers with code
Natural Language Reinforcement Learning
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.
Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models
In the face of uncertainty, the ability to seek information is of fundamental importance.
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning
This paper presents a general framework model for integrating and learning structured reasoning into AI agents' policies.
Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training
Empirical results across reasoning, planning, alignment, and decision-making tasks show that TS-LLM outperforms existing approaches and can handle trees with a depth of 64.
ChessGPT: Bridging Policy Learning and Language Modeling
Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.
Heterogeneous-Agent Reinforcement Learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research.
Contextual Transformer for Offline Meta Reinforcement Learning
Firstly, we propose prompt tuning for offline RL, where a context vector sequence is concatenated with the input to guide the conditional policy generation.
TorchOpt: An Efficient Library for Differentiable Optimization
TorchOpt further provides a high-performance distributed execution runtime.
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning
In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects.
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforcement learner to achieve fast adaptations.
Neural Auto-Curricula in Two-Player Zero-Sum Games
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Multi-agent Reinforcement Learning
Vocal Bursts Valence Prediction
CMML: Contextual Modulation Meta Learning for Cold-Start Recommendation
Meta learning, especially gradient based one, can be adopted to tackle this problem by learning initial parameters of the model and thus allowing fast adaptation to a specific task from limited data examples.
Neural Auto-Curricula
When solving two-player zero-sum games, multi-agent reinforcement learning (MARL) algorithms often create populations of agents where, at each iteration, a new agent is discovered as the best response to a mixture over the opponent population.
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning
How to collect informative trajectories of which the corresponding context reflects the specification of tasks?
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement Learning
In this work, we propose a deep reinforcement learning based method to reconstruct the corrupted images with meaningful pixel-wise operations (e. g. edge enhancing filters), so that the reconstruction process is transparent to users.
