Many batch RL algorithms underperform both online DQN and the behavioral policy on Atari; an adapted discrete-action BCQ outperforms the others tested.
Rainbow: Combining Improvements in Deep Reinforcement Learning
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.
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Disentangled Skill Embeddings (DSE) is a variational inference framework for multi-task RL using shared parameters and task-specific latent embeddings for generalization to unseen conditions and as skills in hierarchical RL.
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
AWR learns policies via advantage-weighted supervised regression on actions, achieving competitive off-policy performance on Gym tasks and strong results from static data alone.
Describes a hierarchical RL method for multi-agent learning in team sports games aiming for human-like agents, reporting preliminary results that show promise.
citing papers explorer
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Benchmarking Batch Deep Reinforcement Learning Algorithms
Many batch RL algorithms underperform both online DQN and the behavioral policy on Atari; an adapted discrete-action BCQ outperforms the others tested.
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Disentangled Skill Embeddings for Reinforcement Learning
Disentangled Skill Embeddings (DSE) is a variational inference framework for multi-task RL using shared parameters and task-specific latent embeddings for generalization to unseen conditions and as skills in hierarchical RL.
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Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
LQL turns n-step action-sequence lower bounds into a practical hinge-loss stabilizer for off-policy Q-learning without extra networks or forward passes.
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Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
AWR learns policies via advantage-weighted supervised regression on actions, achieving competitive off-policy performance on Gym tasks and strong results from static data alone.
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On Multi-Agent Learning in Team Sports Games
Describes a hierarchical RL method for multi-agent learning in team sports games aiming for human-like agents, reporting preliminary results that show promise.