#RobotLearning lecture update. These next lectures cover deep #qlearning fundamentals quickly and then get into the challenges of training #largemodels, maintaining the contraction property with target networks, network structure, and better optimizers.

#DeepQlearning for continuous actions is key for controlling many types of #robots, but it has been tricky to train #largeModels to achieve those performance gains. In these lectures, I cover the fundamentals and explain how new research is bending the rules of the #deadlytriad to advance #scaling.

Are we making the process of QLearning into a Tamagochi? Yes.
Did we make it depressed? Yeeeeah?? 😬

Part 1 Fundamentals of Continuous Deep Q learning: youtu.be/OjVXsQqg7mc
Part 2 More fundamentals and Scaling Deep Q learning: youtu.be/OjVXsQqg7mc

#snakegame on a #protogen #fursuit (pretrained q-learning system)
#furry #fursuits #furryfandom #ai #ml #machinelearning #artificialintelligence #qlearning #reinforcementlearning

youtu.be/19fCOoLaQ0A

#machinelearning #epsilongreedy #qlearning #artificialintelligence #pyplot #numpy #python
Some write-up on emergent decaying epsilon-greedy Q-learning in Python.
Conclusion in thread:

CRAN removals: backShift bingat ccmm censCov ClusterGVis colf complexplus gains gamreg QLearning Relatedness RpeakChrom SigOptR Sim.PLFN SLDAssay TreatmentSelection TSMN TSMSN UniIsoRegression Weighted.Desc.Stat #rstats

A unified ODE framework has been introduced to improve convergence analysis for Q‑learning. The approach unifies continuous‑time perspectives with discrete updates, providing tighter bounds. https://getnews.me/unified-ode-framework-improves-convergence-analysis-for-q-learning/ #ode #qlearning

New gap‑dependent analysis of federated Q‑learning delivers a log T regret bound (versus √T) and removes the M S A factor from communication cost. Read more: https://getnews.me/gap-dependent-regret-and-communication-bounds-for-federated-q-learning/ #federatedlearning #qlearning

The new (P)rior(D)yna(F)low framework boosts multi‑LLM task accuracy by 4.05% and cuts workflow construction and inference costs to 30.68%–48.31% of competing methods. Read more: https://getnews.me/ai-workflow-construction-with-q-learning-boosts-agent-performance/ #priordynaflow #qlearning #multillm

This is just silly; I don't know what the author was really expecting here:

"I Asked An Algorithm To Optimize My Life. Here's What Happened", Wired (www.wired.com/story/i-aske...).

#AI #ReinforcementLearning #QLearning #ML #LifeHacks #Optimization

Removed from CRAN: pipe.design (0.5.1), plaqr (2.0), QLearning (0.1.1), qrmix (0.9.0), Relatedness (2.0), rma.exact (0.1.0), roots (1.0), RpeakChrom (1.1.0), seqICP (1.1), SigOptR (0.0.1), Sim.PLFN (1.0), SLDAssay (1.8), stablespec (0.3.0)

Just finished a grid world which is home to a QLearning agent. This agent is programmed to locate a nutrient source through reinforcement learning by navigating along a gradient.

github.com/DataScienceF...

Just finished a (very) small reinforcement learning test providing a straightforward look at a QLearning agent navigating a number guessing game with both its successes and unexpected learning challenges.

github.com/DataScienceF...

🆕 Q-Learning from Scratch: 50-Line Agent Beats Random by 94%

#ReinforcementLearning #AI #QLearning
https://tildalice.io/q-learning-from-scratch-50-line-agent/

🆕 RL Basics: MDP to Q-Learning in 5 Diagrams

#ReinforcementLearning #AI #reinforcementlearning #qlearning
https://tildalice.io/rl-basics-mdp-q-learning-5-diagrams/