Reinforcement Learning
Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets
To overcome this limitation, we present a novel approach to enhance RvS methods by integrating intermediate targets.
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement Learning
Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is not feasible.
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning Jianzhun Shao, Y un Qu
MARL in real scenarios is still challenging due to the same safety and efficiency concerns in single-agent setting, then it is worth conducting investigation for offline RL in multi-agent setting.
Doubly Robust Augmented Transfer for Meta-Reinforcement Learning
RL problems through the idea of "learning to learn". Current meta-RL methods can be classified in to two categories. These methods mainly differ in their ways of inference [3, 4, 20]. The other line follows the technique of relabeling that enables sample reuse across tasks, i.e., learning a task Packer et al. apply hindsight relabeling for meta-RL, and propose hindsight task relabeling (HTR) to relabel the trajectories Taking a step further than hindsight relabelling, Wan et al. introduce additionally foresight Huang et al. derive a general form of policy gradient from DR value estimator [29], whereas a DR off-policy actor-critic Kallus et al. propose the doubly robust method to find a robust policy that can Depending on the knowledge to be transferred, these methods in RL can be roughly divided into classes including sampled transitions [32, 33], learned policies or value networks [34, 35, 36, 37], features [38, 39, 40], and skills [41, 42]. Doubly Robust Property for Direct Use of Doubly Robust Estimator We show the doubly robust property of the DR estimator for value function in Eq. (5) in the main text, as follows.
Suggesting Variable Order for Cylindrical Algebraic Decomposition via Reinforcement Learning
The other is GRL-SVO(NUP), a fast heuristic providing a total order directly.
A Finite-Sample Analysis of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
A common feature of these applications is that there are multiple decision makers interacting with each other in a common environment.
SafeDICE: Offline Safe Imitation Learning with Non-Preferred Demonstrations
In this paper, we present a hyperparameter-free offline safe IL algorithm, SafeDICE, that learns safe policy by leveraging the non-preferred demonstrations in the space of stationary distributions. Our algorithm directly estimates the stationary distribution corrections of the policy that imitate the demonstrations excluding the non-preferred behavior.
Replicability in Reinforcement Learning
We initiate the mathematical study of replicability as an algorithmic property in the context of reinforcement learning (RL). We focus on the fundamental setting of discounted tabular MDPs with access to a generative model .
