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Found 5 papers, 1 papers with code
Learning Diverse Policies with Soft Self-Generated Guidance
However, existing methods often require these experiences to be successful and may overly exploit them, which can cause the agent to adopt suboptimal behaviors.
Trajectory-Oriented Policy Optimization with Sparse Rewards
The proposed algorithm undergoes evaluation across extensive discrete and continuous control tasks with sparse and misleading rewards.
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
The sparsity of reward feedback remains a challenging problem in online deep reinforcement learning (DRL).
Adaptive trajectory-constrained exploration strategy for deep reinforcement learning
Deep reinforcement learning (DRL) faces significant challenges in addressing the hard-exploration problems in tasks with sparse or deceptive rewards and large state spaces.
PINF: Continuous Normalizing Flows for Physics-Constrained Deep Learning
The normalization constraint on probability density poses a significant challenge for solving the Fokker-Planck equation.
