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Found 10 papers, 2 papers with code
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
To tackle the distributional mismatch, we leverage the idea of pessimism and use our OPE method to develop an off-policy learning algorithm for finding a desirable policy pair for both Alice and Bob.
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
False Correlation Reduction for Offline Reinforcement Learning
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems.
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert policy on a certain predefined reward set.
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Instrumental variables (IVs), in the context of RL, are the variables whose influence on the state variables are all mediated through the action.
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
To the best of our knowledge, we establish the rate of convergence and global optimality of single-timescale actor-critic with linear function approximation for the first time.
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost.
Sample Elicitation
We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.
Credible Sample Elicitation by Deep Learning, for Deep Learning
While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \sim p(x)$ from agents.
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective
We study reinforcement learning algorithms with nonlinear function approximation in the online setting.
