PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation

no code implementations • 23 Sep 2021 • Alexey Kamenev, Lirui Wang, Ollin Boer Bohan, Ishwar Kulkarni, Bilal Kartal, Artem Molchanov, Stan Birchfield, David Nistér, Nikolai Smolyanskiy

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.

Autonomous Driving Motion Planning +2

Work in Progress: Temporally Extended Auxiliary Tasks

no code implementations • 1 Apr 2020 • Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Our overall conclusions are that TD-AE increases the robustness of the A2C algorithm to the trajectory length and while promising, further study is required to fully understand the relationship between auxiliary task prediction timescale and the agent's performance.

On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

no code implementations • 26 Jul 2019 • Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman.

reinforcement-learning Reinforcement Learning (RL)

Action Guidance with MCTS for Deep Reinforcement Learning

no code implementations • 25 Jul 2019 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency.

reinforcement-learning Reinforcement Learning (RL)

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

no code implementations • 24 Jul 2019 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency.

Atari Games reinforcement-learning +2

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

no code implementations • 22 Jul 2019 • Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling.

reinforcement-learning Reinforcement Learning (RL) +1

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

1 code implementation • 20 Apr 2019 • Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.

Reinforcement Learning (RL)

Safer Deep RL with Shallow MCTS: A Case Study in Pommerman

no code implementations • 10 Apr 2019 • Bilal Kartal, Pablo Hernandez-Leal, Chao GAO, Matthew E. Taylor

In this paper, we shed light into the reasons behind this failure by exemplifying and analyzing the high rate of catastrophic events (i. e., suicides) that happen under random exploration in this domain.

reinforcement-learning Reinforcement Learning (RL) +1

Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

no code implementations • 30 Nov 2018 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor

Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power.

reinforcement-learning Reinforcement Learning (RL)

A Survey and Critique of Multiagent Deep Reinforcement Learning

no code implementations • 12 Oct 2018 • Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor

The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature.

reinforcement-learning Reinforcement Learning (RL)