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Found 7 papers, 4 papers with code
Monitored Markov Decision Processes
In this paper, we formalize a novel but general RL framework - Monitored MDPs - where the agent cannot always observe rewards.
The Unsurprising Effectiveness of Pre-Trained Vision Models for Control
In this context, we revisit and study the role of pre-trained visual representations for control, and in particular representations trained on large-scale computer vision datasets.
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration
In this setup, the agent first learns to explore across many environments without any extrinsic goal in a task-agnostic manner.
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
Empirical results on classic and novel benchmarks show that the proposed approach outperforms existing methods in environments with sparse rewards, especially in the presence of rewards that create suboptimal modes of the objective function.
TD-Regularized Actor-Critic Methods
Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability.
Policy Search with High-Dimensional Context Variables
A naive application of unsupervised dimensionality reduction methods to the context variables, such as principal component analysis, is insufficient as task-relevant input may be ignored.
Multi-objective Reinforcement Learning with Continuous Pareto Frontier Approximation Supplementary Material
The paper is about learning a continuous approximation of the Pareto frontier in Multi-Objective Markov Decision Problems (MOMDPs).
Multi-Objective Reinforcement Learning
reinforcement-learning
