Sample Efficient Actor-critic With Experience Replay

Abstract

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keywang2016sample

Related papers

• Frugal Actor-critic: Sample Efficient Off-policy Deep Reinforcement Learning Using Unique Experiences (2024)0.00
• The Reactor: A Fast And Sample-efficient Actor-critic Agent For Reinforcement Learning (2017)0.00
• CUER: Corrected Uniform Experience Replay For Off-policy Continuous Deep Reinforcement Learning Algorithms (2024)0.00
• Stabilising Experience Replay For Deep Multi-agent Reinforcement Learning (2017)0.00
• Sample-efficient Model-free Reinforcement Learning With Off-policy Critics (2019)9.60
• Shared Experience Actor-critic For Multi-agent Reinforcement Learning (2020)0.00
• Behavior-guided Actor-critic: Improving Exploration Via Learning Policy Behavior Representation For Deep Reinforcement Learning (2021)0.00
• Replay For Safety (2021)0.00