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Found 7 papers, 6 papers with code
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone.
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement Learning
Multi-objective reinforcement learning algorithms (MORL) extend standard reinforcement learning (RL) to scenarios where agents must optimize multiple---potentially conflicting---objectives, each represented by a distinct reward function.
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
Finally, we introduce a bound that characterizes the maximum utility loss (with respect to the optimal solution) incurred by the partial solutions computed by our method throughout learning.
MO-Gym: A Library of Multi-Objective Reinforcement Learning Environments
We introduce MO-Gym, an extensible library containing a diverse set of multi-objective reinforcement learning environments.
Optimistic Linear Support and Successor Features as a Basis for Optimal Policy Transfer
If reward functions are expressed linearly, and the agent has previously learned a set of policies for different tasks, successor features (SFs) can be exploited to combine such policies and identify reasonable solutions for new problems.
Minimum-Delay Adaptation in Non-Stationary Reinforcement Learning via Online High-Confidence Change-Point Detection
Non-stationary environments are challenging for reinforcement learning algorithms.
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control
In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent.
