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Found 7 papers, 4 papers with code
Mean Actor Critic
We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action continuous-state reinforcement learning.
Ranked #1 on
Continuous Control
on Cart Pole (OpenAI Gym)
Deep Abstract Q-Networks
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards.
Implementing the Deep Q-Network
The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and building point for much deep reinforcement learning research.
Provably Safe PAC-MDP Exploration Using Analogies
A key challenge in applying reinforcement learning to safety-critical domains is understanding how to balance exploration (needed to attain good performance on the task) with safety (needed to avoid catastrophic failure).
Enforcing robust control guarantees within neural network policies
When designing controllers for safety-critical systems, practitioners often face a challenging tradeoff between robustness and performance.
Projected Off-Policy Q-Learning (POP-QL) for Stabilizing Offline Reinforcement Learning
A key problem in off-policy Reinforcement Learning (RL) is the mismatch, or distribution shift, between the dataset and the distribution over states and actions visited by the learned policy.
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
In many real-world problems, there is a limited set of training data, but an abundance of unlabeled data.
