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Found 5 papers, 2 papers with code
Blockwise Sequential Model Learning for Partially Observable Reinforcement Learning
This paper proposes a new sequential model learning architecture to solve partially observable Markov decision problems.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning
In this paper, we consider intrinsic reward generation for sparse-reward reinforcement learning based on model prediction errors.
Population-Guided Parallel Policy Search for Reinforcement Learning
In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information.
Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning
In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models.
Interactive Parallel Exploration for Reinforcement Learning in Continuous Action Spaces
In this paper, a new interactive parallel learning scheme is proposed to enhance the performance of off-policy continuous-action reinforcement learning.
