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Found 11 papers, 8 papers with code
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
This kind of data-driven optimization (DDO) presents a range of challenges beyond those in standard prediction problems, since we need models that successfully predict the performance of new designs that are better than the best designs seen in the training set.
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
In this paper, we reinterpret IQL as an actor-critic method by generalizing the critic objective and connecting it to a behavior-regularized implicit actor.
Heterogeneous-Agent Reinforcement Learning
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in AI research.
Discovered Policy Optimisation
We refer to the immediate result as Learnt Policy Optimisation (LPO).
Heterogeneous-Agent Mirror Learning: A Continuum of Solutions to Cooperative MARL
The necessity for cooperation among intelligent machines has popularised cooperative multi-agent reinforcement learning (MARL) in the artificial intelligence (AI) research community.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
In this paper, we introduce a novel architecture named Multi-Agent Transformer (MAT) that effectively casts cooperative multi-agent reinforcement learning (MARL) into SM problems wherein the task is to map agents' observation sequence to agents' optimal action sequence.
Understanding Value Decomposition Algorithms in Deep Cooperative Multi-Agent Reinforcement Learning
Value function decomposition is becoming a popular rule of thumb for scaling up multi-agent reinforcement learning (MARL) in cooperative games.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Mirror Learning: A Unifying Framework of Policy Optimisation
In contrast, in this paper we introduce a novel theoretical framework, named Mirror Learning, which provides theoretical guarantees to a large class of algorithms, including TRPO and PPO.
Multi-Agent Constrained Policy Optimisation
To fill these gaps, in this work, we formulate the safe MARL problem as a constrained Markov game and solve it with policy optimisation methods.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
In this paper, we extend the theory of trust region learning to MARL.
Settling the Variance of Multi-Agent Policy Gradients
In multi-agent RL (MARL), although the PG theorem can be naturally extended, the effectiveness of multi-agent PG (MAPG) methods degrades as the variance of gradient estimates increases rapidly with the number of agents.
