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Found 8 papers, 3 papers with code
Infinite Photorealistic Worlds using Procedural Generation
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
These changes are often spurious and unrelated to the underlying problem, such as background shifts for visual input agents.
On the Estimation Bias in Double Q-Learning
Double Q-learning is a classical method for reducing overestimation bias, which is caused by taking maximum estimated values in the Bellman operation.
Off-Policy Reinforcement Learning with Delayed Rewards
We study deep reinforcement learning (RL) algorithms with delayed rewards.
DOP: Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning
Value decomposition is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings.
Off-Policy Multi-Agent Decomposed Policy Gradients
In this paper, we investigate causes that hinder the performance of MAPG algorithms and present a multi-agent decomposed policy gradient method (DOP).
Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization
Value factorization is a popular and promising approach to scaling up multi-agent reinforcement learning in cooperative settings, which balances the learning scalability and the representational capacity of value functions.
