RS-Rainbow learns to interpret Atari agent decisions.
problem Difficult to understand how Atari agents make decisions.
method End-to-end trainable network with attention module.
result Improves model interpretability and performance.
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4 results for “DeepRL”
Proposes a conservative exploration method for RL agents.
problem Guaranteeing performance of exploratory policies in RL.
method Importance sampling for off-policy policy evaluation.
result Derives a regret bound ensuring no conservative constraint violation.
All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforcement Learning (RL) by Kaelbling (1993). In prior work this was mostly explored in small-state RL problems that allowed tabular representati…
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learningcs.LG
Improved DRL performance with novel pre-training method.
problem Data inefficiency in DRL algorithms.
method Jointly pre-training with supervised, autoencoder, and value losses.
result Significantly improved learning performance in Atari games.