"Reinforcement Learning with Action Sequence for Data-Efficient Robot Learning"

Teaching robots to plan ahead by learning from sequences of actions instead of single steps.

Robots learn better when they think about multiple moves ahead, just like chess players

This paper introduces CQN-AS (Coarse-to-fine Q-Network with Action Sequence), a reinforcement learning algorithm that learns Q-values over action sequences instead of single actions. This helps handle noisy trajectories from exploration or demonstrations better, improving data efficiency in robotic tasks.

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https://arxiv.org/abs/2411.12155

🤖 Original Problem:

Traditional reinforcement learning struggles with data efficiency in robotic tasks due to noisy trajectories from exploration or human demonstrations. This makes it hard to learn good value functions that understand action consequences.

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🔧 Solution in this Paper:

→ CQN-AS learns a critic network that outputs Q-values for a sequence of actions, not just single actions

→ It processes features for each sequence step and aggregates them using a recurrent neural network

→ The network explicitly learns consequences of executing current and future actions together

→ It builds on CQN algorithm that zooms into continuous action space in coarse-to-fine manner

→ Uses temporal ensemble for action execution by computing weighted average of recent actions

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💡 Key Insights:

→ Predicting action sequences helps handle noisy, multi-modal expert demonstrations better

→ Learning Q-values over sequences enables better understanding of action consequences

→ Temporal ensemble of actions improves robotic control performance

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📊 Results:

→ Outperforms various RL and BC baselines on BiGym benchmark with human demonstrations

→ Shows superior performance on HumanoidBench tasks without demonstrations

→ Matches or exceeds baseline performance on RLBench tasks with synthetic demonstrations