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Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning
This work proposes a general method for efficient exploration by sharing experience amongst agents by applying experience sharing in an actor-critic framework and finds that it consistently outperforms two baselines and two state-of-the-art algorithms by learning in fewer steps and converging to higher returns. Expand
Comparative Evaluation of Multi-Agent Deep Reinforcement Learning Algorithms
This work evaluates and compares three different classes of MARL algorithms in a diverse range of multi-agent learning tasks and shows that algorithm performance depends strongly on environment properties and no algorithm learns efficiently across all learning tasks. Expand
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks
This work consistently evaluate and compare three different classes of MARL algorithms in a diverse range of cooperative multi-agent learning tasks, and provides insights regarding the effectiveness of different learning approaches. Expand
Decoupling Exploration and Exploitation in Reinforcement Learning
It is shown that DeRL is more robust to scaling and speed of decay of intrinsic rewards and converges to the same evaluation returns than intrinsically motivated baselines in fewer interactions. Expand
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
Deep reinforcement learning (RL) agents that exist in high-dimensional state spaces, such as those composed of images, have interconnected learning burdens. Agents must learn an action-selectionExpand