A diffusion model trained on DOOM play sessions generates stable real-time interactive game frames at 20 FPS with quality near lossy JPEG.
Stable-baselines3: Reliable reinforcement learning implementations
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.LG 3representative citing papers
Shows entropy coupling limits DSAC on discrete tasks and introduces a generalized actor-critic framework with m-step critics and novel entropy-regularized objectives that perform robustly on Atari.
RGoT uses RL to adaptively generate task-specific graphs of operations for GoT-style LLM prompting from a human-provided set, with results suggesting feasibility under constraints.
citing papers explorer
-
Diffusion Models Are Real-Time Game Engines
A diffusion model trained on DOOM play sessions generates stable real-time interactive game frames at 20 FPS with quality near lossy JPEG.
-
Dissecting Discrete Soft Actor-Critic: Limitations and Principled Alternatives
Shows entropy coupling limits DSAC on discrete tasks and introduces a generalized actor-critic framework with m-step critics and novel entropy-regularized objectives that perform robustly on Atari.
-
Reinforced Graph of Thoughts: RL-Driven Adaptive Prompting for LLMs
RGoT uses RL to adaptively generate task-specific graphs of operations for GoT-style LLM prompting from a human-provided set, with results suggesting feasibility under constraints.