Search Results for author:
Found 6 papers, 4 papers with code
Policy-Guided Diffusion
Our approach provides an effective alternative to autoregressive offline world models, opening the door to the controllable generation of synthetic training data.
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Either they are too slow for meaningful research to be performed without enormous computational resources, like Crafter, NetHack and Minecraft, or they are not complex enough to pose a significant challenge, like Minigrid and Procgen.
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.
Trust-Region-Free Policy Optimization for Stochastic Policies
In this paper, we show that the trust region constraint over policies can be safely substituted by a trust-region-free constraint without compromising the underlying monotonic improvement guarantee.
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning
In this work, we conduct new analysis demonstrating that SMAC lacks the stochasticity and partial observability to require complex *closed-loop* policies.
Generalization in Cooperative Multi-Agent Systems
Specifically, we study generalization bounds under a linear dependence of the underlying dynamics on the agent capabilities, which can be seen as a generalization of Successor Features to MAS.
