Reinforcement Learning
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis
Logic contains explainable task-solving knowledge that naturally can generalize across similar tasks.
Finite-Time Analysis for Double Q-learning
Theoretical performance of Q-learning has also been intensively explored. The asymptotic convergence has been established in Tsitsiklis (1994); Jaakkola et al. (1994); Borkar and Meyn (2000); Melo (2001); Lee and He (2019).
PerSim: Data-efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators
We perform extensive experiments across several benchmark environments and RL methods.
Autonomous Reinforcement Learning via Subgoal Curricula
Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents.
Behavior From the Void: Unsupervised Active Pre-Training
We empirically evaluate APT by exposing task-specific reward after a long unsupervised pre-training phase.
Robust Imitation of a Few Demonstrations with a Backwards Model
By imitating both demonstrations and these model rollouts, the agent learns the demonstrated paths and how to get back onto these paths.
SUPPLEMENTARY MATERIAL Deep Reinforcement Learning with Stacked Hierarchical Attention for T based Games
Figure 1 shows an example of the raw interface of the game "ztuu", where raw textual observations In this section, we show the first 15 interaction steps of two games: "zork1" and "ztuu". C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 2 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 3 ===== 16 ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: s o u t h Reward: 0 | S c o r e: 0 ===== S t e p 4 ===== ===== 1 . C h o s e n a c t i o n a n d r e w a r d A c t i o n: w e s t Reward: 0 | S c o r e: 0 ===== S t e p 5 ===== ===== 1 .
