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Found 7 papers, 0 papers with code
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
We propose an algorithm that boosts MARL training using the biased action information of other agents based on a friend-or-foe concept.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
PGPS : Coupling Policy Gradient with Population-based Search
Gradient-based policy search algorithms (such as PPO, SAC or TD3) in deep reinforcement learning (DRL) have shown successful results on a range of challenging control tasks.
REMAX: Relational Representation for Multi-Agent Exploration
To train the MARL model effectively without designing the intrinsic reward, we propose a learning-based exploration strategy to generate the initial states of a game.
Does Adam optimizer keep close to the optimal point?
The adaptive optimizer for training neural networks has continually evolved to overcome the limitations of the previously proposed adaptive methods.
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks.
Multi-Agent Actor-Critic with Generative Cooperative Policy Network
We propose an efficient multi-agent reinforcement learning approach to derive equilibrium strategies for multi-agents who are participating in a Markov game.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Learning to Select: Problem, Solution, and Applications
For example, job dispatching in the manufacturing factory is a typical "Learning to Select" problem.
