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Found 6 papers, 2 papers with code
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning Rate
Recent progress has demonstrated that recurrent reinforcement learning (RL), which consists of a context encoder based on recurrent neural networks (RNNs) for unobservable state prediction and a multilayer perceptron (MLP) policy for decision making, can mitigate partial observability and serve as a robust baseline for POMDP tasks.
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
MOREC learns a generalizable dynamics reward function from offline data, which is subsequently employed as a transition filter in any offline MBRL method: when generating transitions, the dynamics model generates a batch of transitions and selects the one with the highest dynamics reward value.
Unified Policy Optimization for Continuous-action Reinforcement Learning in Non-stationary Tasks and Games
This paper addresses policy learning in non-stationary environments and games with continuous actions.
A Survey on Model-based Reinforcement Learning
In this survey, we take a review of MBRL with a focus on the recent progress in deep RL.
Transferable Reward Learning by Dynamics-Agnostic Discriminator Ensemble
In this work, we present a dynamics-agnostic discriminator-ensemble reward learning method (DARL) within the AIL framework, capable of learning both state-action and state-only reward functions.
Offline Model-based Adaptable Policy Learning
Current offline reinforcement learning methods commonly learn in the policy space constrained to in-support regions by the offline dataset, in order to ensure the robustness of the outcome policies.
