The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.
( Image credit: Playing Atari with Deep Reinforcement Learning )
Furthermore, we successfully extend our theoretical framework to maximum-entropy RL by deriving the lower and upper bounds of these value metrics for soft Q-learning, which turn out to be the product of $|\text{TD}|$ and "on-policyness" of the experiences.
We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games.
All four approaches work by perturbing parts of the input and measuring how much this affects the agent's output.
Results: A range of Deep RL agents based on Deep Q networks were tested in this custom environment.
Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.
Temporal information is essential to learning effective policies with Reinforcement Learning (RL).
CONTINUOUS CONTROL MONTEZUMA'S REVENGE OPTICAL FLOW ESTIMATION VIDEO CLASSIFICATION
We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.
Humans can abstract prior knowledge from very little data and use it to boost skill learning.
Moreover, input entropy and information gain correlate more strongly with human similarity than task reward does, suggesting the use of intrinsic objectives for designing agents that behave similarly to human players.
In contrast, asynchronous methods achieve high throughput but suffer from stability issues and lower sample efficiency due to `stale policies.'