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Found 6 papers, 2 papers with code
Average-Reward Learning and Planning with Options
We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs.
Planning with Expectation Models for Control
Our algorithms and experiments are the first to treat MBRL with expectation models in a general setting.
MADRaS : Multi Agent Driving Simulator
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving.
Learning and Planning in Average-Reward Markov Decision Processes
We introduce learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference states, 2) the first proven-convergent off-policy model-free prediction algorithm, and 3) the first off-policy learning algorithm that converges to the actual value function rather than to the value function plus an offset.
Discounted Reinforcement Learning Is Not an Optimization Problem
Discounted reinforcement learning is fundamentally incompatible with function approximation for control in continuing tasks.
RAIL: Risk-Averse Imitation Learning
Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories.
