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The Atari 2600 Games task (and dataset) involves training an agent to achieve high game scores.

( Image credit: Playing Atari with Deep Reinforcement Learning )

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Datasets

Latest papers with code

Revisiting Prioritized Experience Replay: A Value Perspective

5 Feb 2021RLforlife/VER

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.

ATARI GAMES Q-LEARNING

1
05 Feb 2021

Shielding Atari Games with Bounded Prescience

20 Jan 2021HjalmarWijk/bounded-prescience

We present the first exact method for analysing and ensuring the safety of DRL agents for Atari games.

ATARI GAMES AUTONOMOUS DRIVING

0
20 Jan 2021

Benchmarking Perturbation-based Saliency Maps for Explaining Deep Reinforcement Learning Agents

18 Jan 2021belimmer/PerturbationSaliencyEvaluation

All four approaches work by perturbing parts of the input and measuring how much this affects the agent's output.

ATARI GAMES FEATURE IMPORTANCE IMAGE CLASSIFICATION

2
18 Jan 2021

Evolving Reinforcement Learning Algorithms

ICLR 2021 jjgarau/DAGPolicyGradient

Learning from scratch on simple classical control and gridworld tasks, our method rediscovers the temporal-difference (TD) algorithm.

ATARI GAMES META-LEARNING

2
08 Jan 2021

Reinforcement Learning with Latent Flow

6 Jan 2021WendyShang/flare

Temporal information is essential to learning effective policies with Reinforcement Learning (RL).

CONTINUOUS CONTROL MONTEZUMA'S REVENGE OPTICAL FLOW ESTIMATION VIDEO CLASSIFICATION

16
06 Jan 2021

Multi-Agent Trust Region Learning

1 Jan 2021matrl-project/matrl

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.

ATARI GAMES MULTI-AGENT REINFORCEMENT LEARNING Q-LEARNING

2
01 Jan 2021

Augmenting Policy Learning with Routines Discovered from a Demonstration

23 Dec 2020sjtuytc/-AAAI21-RoutineAugmentedPolicyLearning-RAPL-

Humans can abstract prior knowledge from very little data and use it to boost skill learning.

ATARI GAMES IMITATION LEARNING

13
23 Dec 2020

Evaluating Agents without Rewards

21 Dec 2020bfmat/agenteval

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.

ATARI GAMES MINECRAFT

1
21 Dec 2020

High-Throughput Synchronous Deep RL

NeurIPS 2020 IouJenLiu/HTS-RL

In contrast, asynchronous methods achieve high throughput but suffer from stability issues and lower sample efficiency due to `stale policies.'

ATARI GAMES

9
17 Dec 2020