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Found 7 papers, 4 papers with code
CUER: Corrected Uniform Experience Replay for Off-Policy Continuous Deep Reinforcement Learning Algorithms
The utilization of the experience replay mechanism enables agents to effectively leverage their experiences on several occasions.
Actor Prioritized Experience Replay
A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error.
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction Approach
Compared to on-policy counterparts, off-policy model-free deep reinforcement learning can improve data efficiency by repeatedly using the previously gathered data.
Safe and Robust Experience Sharing for Deterministic Policy Gradient Algorithms
Learning in high dimensional continuous tasks is challenging, mainly when the experience replay memory is very limited.
AWD3: Dynamic Reduction of the Estimation Bias
We show through continuous control environments of OpenAI gym that our algorithm matches or outperforms the state-of-the-art off-policy policy gradient learning algorithms.
Off-Policy Correction for Deep Deterministic Policy Gradient Algorithms via Batch Prioritized Experience Replay
In addition, experience replay stores the transitions are generated by the previous policies of the agent that may significantly deviate from the most recent policy of the agent.
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic Methods
We show that in deep actor-critic methods that aim to overcome the overestimation bias, if the reinforcement signals received by the agent have a high variance, a significant underestimation bias arises.
