📉 Loss as a Reward 🎁
In reinforcement learning, designing the “right” reward function can be more challenging than building the model itself. That’s why we explored LaaR (Loss as a Reward), an approach where we forgo traditional RL reward shaping and instead let the supervised training loss itself guide the agent.
Why is this exciting?
• Automatic Reward Signal: By using the classification loss directly, we sidestep the need for hand-crafted reward functions. The agent learns to minimize loss naturally, just like a supervised model, but does so through a sequence of actions that zooms in on the pertinent parts of the input.
• Partial Observability & Efficiency: We’re flipping the paradigm of analyzing all pixels at once. Instead, our agent takes a limited “window” glimpse at the image, much like how humans momentarily focus on different parts of a scene. This is hypothetically more computationally efficient and more biologically plausible. We limit the agent to 10 glimpses, but interestingly, it already recognizes the digit by the third glimpse.
• Bridging RL and Supervised Learning: Rather than treating reinforcement learning and supervised learning as separate paradigms, this approach intertwines them. The RL agent’s policy improvement is driven by the same metric used to train conventional neural networks.
Under the Hood
Decision Transformer: At the core is a Decision Transformer (DT) that handles sequences of states and actions just like language models handle tokens. We feed it the agent’s past observations, actions, and (inverted) loss-based reward.
Curriculum Learning: We take a cue from how students learn, building a solid foundation before tackling advanced material. Initially, the agent sees “easier” digits; tackling too-challenging examples from the get-go could result in confusion and poor learning.
Dynamic Window & Zoom: The environment allows the agent to move a small window across the image and zoom in or out, effectively deciding where to look next.
86.6% Accuracy: This simple yet effective architecture, treating each classification step as an RL step, achieves an 86.6% accuracy on MNIST test.
Implications
• Generalizable Reward: Wherever there’s a differentiable training objective (like cross-entropy loss), we can convert it into a reinforcement signal.
• Guided Exploration: By tying the reward to the loss, the agent explores actions that directly reduce errors, requiring fewer hyperparameters or custom reward hacking.
• Scalability: The idea can be extended to larger images, multi-class tasks, or problems where typical RL reward signals are hard to define.
AIArchitecture Architecture ReinforcementLearning RL…📉 Loss as a Reward 🎁
In reinforcement learning, designing the “right” reward function can be more challenging than building the model itself. That’s why we explored LaaR (Loss as a Reward), an approach where we forgo traditional RL reward shaping and instead let the supervised training loss itself guide the agent.
Why is this exciting?
• Automatic Reward Signal: By using the classification loss directly, we sidestep the need for hand-crafted reward functions. The agent learns to minimize loss naturally, just like a supervised model, but does so through a sequence of actions that zooms in on the pertinent parts of the input.
• Partial Observability & Efficiency: We’re flipping the paradigm of analyzing all pixels at once. Instead, our agent takes a limited “window” glimpse at the image, much like how humans momentarily focus on different parts of a scene. This is hypothetically more computationally efficient and more biologically plausible. We limit the agent to 10 glimpses, but interestingly, it already recognizes the digit by the third glimpse.
• Bridging RL and Supervised Learning: Rather than treating reinforcement learning and supervised learning as separate paradigms, this approach intertwines them. The RL agent’s policy improvement is driven by the same metric used to train conventional neural networks.
Under the Hood
Decision Transformer: At the core is a Decision Transformer (DT) that handles sequences of states and actions just like language models handle tokens. We feed it the agent’s past observations, actions, and (inverted) loss-based reward.
Curriculum Learning: We take a cue from how students learn, building a solid foundation before tackling advanced material. Initially, the agent sees “easier” digits; tackling too-challenging examples from the get-go could result in confusion and poor learning.
Dynamic Window & Zoom: The environment allows the agent to move a small window across the image and zoom in or out, effectively deciding where to look next.
86.6% Accuracy: This simple yet effective architecture, treating each classification step as an RL step, achieves an 86.6% accuracy on MNIST test.
Implications
• Generalizable Reward: Wherever there’s a differentiable training objective (like cross-entropy loss), we can convert it into a reinforcement signal.
• Guided Exploration: By tying the reward to the loss, the agent explores actions that directly reduce errors, requiring fewer hyperparameters or custom reward hacking.
• Scalability: The idea can be extended to larger images, multi-class tasks, or problems where typical RL reward signals are hard to define.
AIArchitecture Architecture ReinforcementLearning RLWW…