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Found 12 papers, 2 papers with code
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets
Trading markets represent a real-world financial application to deploy reinforcement learning agents, however, they carry hard fundamental challenges such as high variance and costly exploration.
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
Work in Progress: Temporally Extended Auxiliary Tasks
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
In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman.
Action Guidance with MCTS for Deep Reinforcement Learning
Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency.
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
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.
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
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.
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
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.
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
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.
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power.
A Survey and Critique of Multiagent Deep Reinforcement Learning
The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature.
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.
