#rl #dl #Quantitative_finance #finance #trading #quant #Ai_Capital_Management #team #deep_rl
https://towardsdatascience.com/applied-deep-reinforcement-learning-in-quantitative-trading-both-momentum-and-market-neutral-c0eef522ea11

#alphafold #deepmind #team #biotech #deep_rl #rl #dl #ml
#harvard
https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientific-discovery

https://ccsp.hms.harvard.edu/wp-content/uploads/2020/11/AlphaFold-at-CASP13-AlQuraishi.pdf

https://www.youtube.com/watch?v=B9PL__gVxLI&ab_channel=YannicKilcher

#rl #dl #paper

Communication in Multi-Agent Reinforcement Learning: Intention Sharing by kim et al

TL;DR: In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generates an imagined trajectory by modeling the environment dynamics and other agents' actions. The imagined trajectory is the simulated future trajectory of each agent based on the learned model of the environment dynamics and other agents and represents each agent's future action plan. Each agent compresses this imagined trajectory capturing its future action plan to generate its intention message for communication by applying an attention mechanism to learn the relative importance of the components in the imagined trajectory based on the received message from other agents. Numeral results show that the proposed IS scheme outperforms other communication schemes in multi-agent reinforcement learning.

Paper: https://openreview.net/pdf?id=qpsl2dR9twy

#reinforcement_learning #rl #drl #gamedev #rl_policy #paper
https://www.youtube.com/watch?v=Nz-X3cCeXVE&ab_channel=TwoMinutePapers

https://www.ea.com/seed/news/cog2021-curiosity-driven-rl-agents

#offline_rl #rl #drl #workflow #cql

A Workflow for Offline Model-Free Robotic
Reinforcement Learning
, Sergey Levine
1 UC Berkeley, 2 Stanford University (∗ Equal Contribution)
aviralk@berkeley.edu, asap7772@berkeley.edu

https://www.youtube.com/watch?v=h9R5LJX9b1I&ab_channel=ConferenceonRobotLearning


https://arxiv.org/abs/2109.10813

#llm #training #dpo #vs #rlhf #ppo #reinforcement_learning #rl #gen_ai #NeurIPS
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://arxiv.org/abs/2305.18290v2

#deepmind #mistral #team #dpo #benchmarks #moe #llm #gen_ai
Mixtral of experts. A high quality Sparse Mixture-of-Experts.
https://mistral.ai/news/mixtral-of-experts

#offline_rl #rl
Revisiting the Minimalist Approach to Offline Reinforcement Learning
https://arxiv.org/abs/2305.09836

#agi #gen_ai #benchmarks
Levels of AGI: Operationalizing Progress on the Path to AGI
https://arxiv.org/abs/2311.02462v2