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Found 4 papers, 3 papers with code
DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards
Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards.
Learning Diverse Options via InfoMax Termination Critic
We consider the problem of autonomously learning reusable temporally extended actions, or options, in reinforcement learning.
Playing Catan with Cross-dimensional Neural Network
Catan is a strategic board game having interesting properties, including multi-player, imperfect information, stochastic, complex state space structure (hexagonal board where each vertex, edge and face has its own features, cards for each player, etc), and a large action space (including negotiation).
Rogue-Gym: A New Challenge for Generalization in Reinforcement Learning
Following these studies, we propose the use of roguelikes as a benchmark for evaluating the generalization ability of RL agents.
