Search Results for author:
Found 11 papers, 7 papers with code
Kimi k1.5: Scaling Reinforcement Learning with LLMs
Moreover, we present effective long2short methods that use long-CoT techniques to improve short-CoT models, yielding state-of-the-art short-CoT reasoning results -- e. g., 60. 8 on AIME, 94. 6 on MATH500, 47. 3 on LiveCodeBench -- outperforming existing short-CoT models such as GPT-4o and Claude Sonnet 3. 5 by a large margin (up to +550%).
Joint Policy Search for Multi-agent Collaboration with Imperfect Information
Based on this, we propose Joint Policy Search(JPS) that iteratively improves joint policies of collaborative agents in imperfect information games, without re-evaluating the entire game.
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games
This paper presents ReBeL, a general framework for self-play reinforcement learning and search that provably converges to a Nash equilibrium in any two-player zero-sum game.
Polygames: Improved Zero Learning
Since DeepMind's AlphaZero, Zero learning quickly became the state-of-the-art method for many board games.
All Simulations Are Not Equal: Simulation Reweighing for Imperfect Information Games
We use simulation reweighing in the playing phase of the game contract bridge, and show that it outperforms previous state-of-the-art Monte Carlo simulation based methods, and achieves better play per decision.
Simple is Better: Training an End-to-end Contract Bridge Bidding Agent without Human Knowledge
While playing is relatively easy for modern software, bidding is challenging and requires agents to learn a communication protocol to reach the optimal contract jointly, with their own private information.
Hierarchical Decision Making by Generating and Following Natural Language Instructions
We explore using latent natural language instructions as an expressive and compositional representation of complex actions for hierarchical decision making.
Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
We analyze the dynamics of training deep ReLU networks and their implications on generalization capability.
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero
The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy.
Latent forward model for Real-time Strategy game planning with incomplete information
Model-free deep reinforcement learning approaches have shown superhuman performance in simulated environments (e. g., Atari games, Go, etc).
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment.
