Reinforcement Learning
AdaptableAgentPopulations viaaGenerativeModelofPolicies
In reinforcement learning (RL), it is common to learn a single policy that fits an environment.
Provably Efficient Offline Goal-Conditioned Reinforcement Learning with General Function Approximation and Single-Policy Concentrability
Goal-conditioned reinforcement learning (GCRL) refers to learning general-purpose skills that aim to reach diverse goals.
216f44e2d28d4e175a194492bde9148f-Paper.pdf
We assume the environment modeled as discrete-time factored-action MDP (FA-MDP)M = hS,A,P,R,ฮณi where S is the set of states s, A is the set of vector-represented actionsa = (a1,...,am),P(s0|s,a) = Pr(st+1 = s0|st = s,at = a)isthe transition probability,R(s,a) R is the immediate reward for taking actiona in state s, and ฮณ [0,1) is the discount factor.
InformationDirectedRewardLearning forReinforcementLearning
From such expensive feedback, we aim to learn a model of the reward that allows standard RL algorithms to achieve high expected returnswith as few expert queries as possible.
EpisodicMulti-agentReinforcementLearningwith Curiosity-drivenExploration
Efficient exploration in deep cooperativemulti-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
EpisodicMulti-agentReinforcementLearningwith Curiosity-drivenExploration
Efficient exploration in deep cooperativemulti-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
