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Found 11 papers, 2 papers with code
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years.
Optimally Solving Two-Agent Decentralized POMDPs Under One-Sided Information Sharing
Optimally solving decentralized partially observable Markov decision processes under either full or no information sharing received significant attention in recent years.
LAPTNet-FPN: Multi-scale LiDAR-aided Projective Transform Network for Real Time Semantic Grid Prediction
Semantic grids can be useful representations of the scene around an autonomous system.
LAPTNet: LiDAR-Aided Perspective Transform Network
Semantic grids are a useful representation of the environment around a robot.
HSVI for zs-POSGs using Concavity, Convexity and Lipschitz Properties
Dynamic programming and heuristic search are at the core of state-of-the-art solvers for sequential decision-making problems.
Learning to plan with uncertain topological maps
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy.
On Bellman's Optimality Principle for zs-POSGs
Many non-trivial sequential decision-making problems are efficiently solved by relying on Bellman's optimality principle, i. e., exploiting the fact that sub-problems are nested recursively within the original problem.
EgoMap: Projective mapping and structured egocentric memory for Deep RL
The EgoMap architecture incorporates several inductive biases including a differentiable inverse projection of CNN feature vectors onto a top-down spatially structured map.
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations.
rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
In this paper, we focus on POMDPs and ρ-POMDPs with λ ρ -Lipschitz reward function, and demonstrate that, for finite horizons, the optimal value function is Lipschitz-continuous.
Learning to Act in Decentralized Partially Observable MDPs
We address a long-standing open problem of reinforcement learning in decentralized partially observable Markov decision processes.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
