This chapter concludes three perspectives on multi-agent reinforcement learning (MARL): (1) cooperative MARL, which performs mutual interaction between cooperative agents; (2) equilibrium-based MARL, which focuses on equilibrium solutions among gaming agents; and (3) best-response MARL, which suggests a no-regret policy against other competitive agents. Then the authors present a general framew...

Multi-Agent Reinforcement Learning (MARL) is a widely used technique for optimization in decentralised control problems. However, most applications of MARL are in static environments, and are not suitable when agent behaviour and environment conditions are dynamic and uncertain. Addressing uncertainty in such environments remains a challenging problem for MARL-based systems. The dynamic nature ...

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents’ decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions according to a probability distribution). Weighted Policy Learner (WPL) is a MARL algorithm that belongs to this subset a...

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents’ decisions. Only a subset of these MARL algorithms both do not require agents to know the underlying environment and can learn a stochastic policy (a policy that chooses actions according to a probability distribution). Weighted Policy Learner (WPL) is a MARL algorithm that belongs to this subset a...

Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents’ decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards they received. We introduce a new MARL algorithm ...

In this paper, we examine the application of Multi-Agent Reinforcement Learning (MARL) to a Dynamic Economic Emissions Dispatch problem. This is a multi-objective problem domain, where the conflicting objectives of fuel cost and emissions must be minimised. We evaluate the performance of several different MARL credit assignment structures in this domain, and our experimental results show that M...

The use of narrow beam filter radiometers with lidars goes back some considerable time. The CSIRO Marl< I radiometer was designed and constructed in 1970 (Platt 1971). Since then, improved versions (Marl< II and Marl< 111) have been constructed (Platt et al. 1987). Using the URAD method, much information has been obtained on the optical properties of cirrus (e.g., Platt et al. 1987, Platt and H...

Multi-agent reinforcement learning (MARL) is an emerging area of research. However, it lacks two important elements: a coherent view on MARL, and a well-defined problem objective. We demonstrate these points by introducing three phenomena, social norms, teaching, and bounded rationality, which are inadequately addressed by the previous research. Based on the ideas of bounded rationality, we def...

We conducted geochronologic and pollen analyses from sediment cores collected in solution holes within marl prairies of Big Cypress National Preserve to reconstruct vegetation patterns of the last few centuries and evaluate the stability and longevity of marl prairies within the greater Everglades ecosystem. Based on radiocarbon dating and pollen biostratigraphy, these cores contain sediments d...

son Lake are all within a few kilometres of the Skeena River between Terrace and Old Harelton (Figure 5-3I ) . The tern1 marl is used in thih report to indicate a friable mixture of greater than 40 per cent calcium carbonate together with insoluble detritus and noncarbonate plant material. The colour is usually white or buff, but grey to brown or black shades occur as the organic content increa...