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The ANTHBOT N8 mows, mulches, collects grass clippings and removes leaves. Up to 1,500 m, no boundary wire required, 23-liter grass catcher. One robot handles mowing, clippings and fallen leaves. Robotic lawn mowers take the hard work out of mowing, but they rarely handle the cleanup. Most models rely exclusively on mulching, cutting the grass and leaving the clippings on the lawn.

We earn a commission if you buy something through an affiliate link. Freelancers are getting buried with'soulless' AI slop cleanup: 'It's a shame we need to do it' As more companies turn to AI, they're hiring freelancers to cleanup its mistakes rather than create original work L isa, a freelance graphic designer based in Spain, noticed a shift in her work after the release of ChatGPT in 2022. She went from receiving slow one-off jobs creating logos and packaging to an onslaught of requests asking her to fix versions that were generated by artificial intelligence - from sharpening fuzzy images for printing to turning flawed designs into usable files. By 2025, Lisa, who asked not to be fully named to avoid solicitations, said 90% of her incoming logo and packaging design requests required cleaning up AI-generated content, work that accounted for 60% to 70% of her annual income. But the grind was exhausting.

Multi-agent reinforcement learning (MARL) has emerged as a powerful framework for modeling autonomous agents that independently optimize their individual objectives. However, in mixed-motive MARL environments, rational self-interested behaviors often lead to collectively suboptimal outcomes situations commonly referred to as social dilemmas. A key challenge in addressing social dilemmas lies in accurately quantifying and representing them in a numerical form that captures how self-interested agent behaviors impact social welfare. To address this challenge, externalities in the economic concept is adopted and extended to denote the unaccounted-for impact of one agent's actions on others, as a means to rigorously quantify social dilemmas. Based on this measurement, a novel method, Learning Optimal Pigovian Tax (LOPT) is proposed. Inspired by Pigovian taxes, which are designed to internalize externalities by imposing cost on negative societal impacts, LOPT employs an auxiliary tax agent that learns an optimal Pigovian tax policy to reshape individual rewards aligned with social welfare, thereby promoting agent coordination and mitigating social dilemmas. We support LOPT with theoretical analysis and validate it on standard MARL benchmarks, including Escape Room and Cleanup. Results show that by effectively internalizing externalities that quantify social dilemmas, LOPT aligns individual objectives with collective goals, significantly improving social welfare over state-of-the-art baselines.

Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.

A.1 Costforincentivization We justify the way in which LIO accounts for the cost of incentivization as follows. However, both the reward-giverand recipients require sufficient time tolearn the effect ofincentives,which means that too large anα would lead to the degenerate result ofrηi = 0. On the other extreme, α = 0means there isno penalty and may result inprofligate incentivization that serves no useful purpose. Let θi for i {1,2} denote each agent's probability of taking the cooperative action. Each plot has afixed value for the incentive givenfortheotheraction. Each agent observesallagents' positions andcanmoveamong thethree available states: lever, start, and door.

Much of this effort has focused on the single-agent setting, in which an agent maximizes a predefined extrinsic reward function.

CleanMyMac itself hooks into macOS's "Allow in the Background" framework here, so it's playing by Apple's rules rather than working around them. You could do most of this via System Settings and a lot of manual digging, but the point here is visibility: you see what's running, how heavy it is, and you can trim without spelunking through multiple folders.

Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation.