Add to ORKGReinforcement learning (RL) agents are particularly hard to train when rewards are sparse. One common solution is to use intrinsic rewards to encourage agents to explore their environment. However, recent intrinsic exploration methods often use state-based novelty measures which reward low-level exploration and may not scale to domains requiring more abstract skills. Instead, we explore natural language as a general medium for highlighting relevant abstractions in an environment. Unlike previous work, we evaluate whether language can improve over existing exploration methods by directly extending (and comparing to) competitive intrinsic exploration baselines: AMIGo (Campero et al., 2021) and NovelD (Zhang et al., 2021). These language-based...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Natural language instruction following is paramount to enable collaboration between artificial agent...
Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration metho...
It has been a long-standing dream to design artificial agents that explore their environment efficie...
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-...
International audienceAutonomous reinforcement learning agents, like children, do not have access to...
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional...
International audienceReinforcement learning (RL) in long horizon and sparse reward tasks is notorio...
Building intelligent agents that can help humans accomplish everyday tasks, such as a personal robot...
Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement le...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...
Effectively exploring the environment is a key challenge in reinforcement learning (RL). We address ...
Natural language instruction following is paramount to enable collaboration between artificial agent...
Effective exploration is a challenge in reinforcement learning (RL). Novelty-based exploration metho...
It has been a long-standing dream to design artificial agents that explore their environment efficie...
We present AIRS: Automatic Intrinsic Reward Shaping that intelligently and adaptively provides high-...
International audienceAutonomous reinforcement learning agents, like children, do not have access to...
To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional...
International audienceReinforcement learning (RL) in long horizon and sparse reward tasks is notorio...
Building intelligent agents that can help humans accomplish everyday tasks, such as a personal robot...
Tasks with large state space and sparse rewards present a longstanding challenge to reinforcement le...
Exploration is essential in reinforcement learning, particularly in environments where external rewa...
We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If...
Sparse reward is one of the biggest challenges in reinforcement learning (RL). In this paper, we pro...
International audienceDespite their apparent importance for the acquisition of fullfledged human int...
Efficient exploration is important for reinforcement learners to achieve high rewards. In multi-agen...