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Found 9 papers, 5 papers with code
Discovering Temporally-Aware Reinforcement Learning Algorithms
We propose a simple augmentation to two existing objective discovery approaches that allows the discovered algorithm to dynamically update its objective function throughout the agent's training procedure, resulting in expressive schedules and increased generalization across different training horizons.
JaxMARL: Multi-Agent RL Environments in JAX
This not only enables GPU acceleration, but also provides a more flexible MARL environment, unlocking the potential for self-play, meta-learning, and other future applications in MARL.
Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design
Recently, it has been shown that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks.
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
We show that the additive term is bounded proportional to the Lipschitz constant of the value function, which offers theoretical grounding for spectral normalization of critic weights.
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning
By enabling agents to communicate, recent cooperative multi-agent reinforcement learning (MARL) methods have demonstrated better task performance and more coordinated behavior.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Proximal Learning With Opponent-Learning Awareness
This problem is especially pronounced in the opponent modeling setting, where the opponent's policy is unknown and must be inferred from observations; in such settings, LOLA is ill-specified because behaviorally equivalent opponent policies can result in non-equivalent updates.
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities
Deep Reinforcement Learning (RL) has recently produced impressive results in a series of settings such as games and robotics.
A Fine-Tuning Approach to Belief State Modeling
Furthermore, because this specialization occurs after the action or policy has already been decided, BFT does not require the belief model to process it as input.
Zero-Shot Coordination via Semantic Relationships Between Actions and Observations
An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations.
